From 3cc3406674b2fc172496111406053e1791244d4f Mon Sep 17 00:00:00 2001
From: Roy Harnish <royharnish@gmail.com>
Date: Sat, 26 Sep 2026 11:25:44 -0700
Subject: [PATCH 1/4] settings.py: every FIGLIB_* switch in one place;
 profiles; peak refinement on

- settings.get reads environment, then a profile (configs/<name>.toml via FIGLIB_PROFILE,
  or a module main()'s default), then the default. Nothing is written to os.environ, so
  import order no longer changes results: the setdefault blocks in coverage, bias,
  fig_bearing and terrain_range become default_profile("calibrated") in their __main__.
- provenance.record stores the whole resolved set, defaults included, with the profile's
  name and hash; configs/ counts toward the dirty-tree check.
- FIGLIB_REFINE_KM defaults to 0.01: solve re-finds its peak off the 0.4 km grid unless
  set to 0. Every published number moves with the coordinated re-run that follows.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 configs/calibrated.toml     |   5 ++
 configs/recent.toml         |   5 ++
 src/figlib/accumulate.py    |   4 +-
 src/figlib/bias.py          |   7 +--
 src/figlib/corpus.py        |   6 +-
 src/figlib/coverage.py      |  12 ++--
 src/figlib/falsealarm.py    |   4 +-
 src/figlib/fig_bearing.py   |   8 +--
 src/figlib/geolocate.py     |  39 +++++++-----
 src/figlib/geom.py          |   8 +--
 src/figlib/pose_ledger.py   |   9 +--
 src/figlib/provenance.py    |  13 ++--
 src/figlib/recent.py        |   5 +-
 src/figlib/settings.py      | 122 ++++++++++++++++++++++++++++++++++++
 src/figlib/terrain_range.py |  18 +++---
 tests/test_geolocation.py   |   4 +-
 tests/test_settings.py      |  86 +++++++++++++++++++++++++
 17 files changed, 288 insertions(+), 67 deletions(-)
 create mode 100644 configs/calibrated.toml
 create mode 100644 configs/recent.toml
 create mode 100644 src/figlib/settings.py
 create mode 100644 tests/test_settings.py

diff --git a/configs/calibrated.toml b/configs/calibrated.toml
new file mode 100644
index 0000000..9f4e9cd
--- /dev/null
+++ b/configs/calibrated.toml
@@ -0,0 +1,5 @@
+# The calibrated camera model: the star-measured fisheye lens and per-camera star poses
+# from data/meta/pose_ledger.json. coverage, bias, fig_bearing and terrain_range run under
+# this profile unless FIGLIB_PROFILE names another; anything set in the environment wins.
+FIGLIB_LENS = "fisheye"
+FIGLIB_POSE_LEDGER = "1"
diff --git a/configs/recent.toml b/configs/recent.toml
new file mode 100644
index 0000000..84f79ed
--- /dev/null
+++ b/configs/recent.toml
@@ -0,0 +1,5 @@
+# The calibrated camera model on the extra CDN cameras (recent.py): the recent corpus's own
+# pose ledger, which adds the star solves of cameras FIgLib never annotated.
+FIGLIB_LENS = "fisheye"
+FIGLIB_POSE_LEDGER = "1"
+FIGLIB_POSE_LEDGER_PATH = "out/recent/pose_ledger.json"
diff --git a/src/figlib/accumulate.py b/src/figlib/accumulate.py
index 5a76b98..78f33e7 100644
--- a/src/figlib/accumulate.py
+++ b/src/figlib/accumulate.py
@@ -30,7 +30,6 @@
 from __future__ import annotations
 
 import json
-import os
 import math
 from pathlib import Path
 
@@ -40,13 +39,14 @@
 from .geom import bearing_grid, haversine_km, load_cams, offset_bearing_deg
 
 from . import corpus as C
+from . import settings
 
 ROOT = Path(__file__).resolve().parents[2]
 META = C.current().meta
 # Which detection pass to score. Overridable so the Core ML variants run through this
 # exact pipeline rather than a parallel one -- the point of the quantization study is a
 # paired comparison, and a second implementation would be a second source of difference.
-YOLO_DIR = Path(os.environ.get("FIGLIB_DETS", C.current().dets))
+YOLO_DIR = Path(settings.get("FIGLIB_DETS") or C.current().dets)
 
 SIGMA_DEG = 2.0
 CONF_FLOOR = 0.10       # below this a detection is treated as pure noise
diff --git a/src/figlib/bias.py b/src/figlib/bias.py
index db0dbfa..06d74f9 100644
--- a/src/figlib/bias.py
+++ b/src/figlib/bias.py
@@ -25,17 +25,13 @@
 """
 from __future__ import annotations
 
-import os
-
-os.environ.setdefault("FIGLIB_LENS", "fisheye")
-os.environ.setdefault("FIGLIB_POSE_LEDGER", "1")
-
 import itertools
 import json
 import math
 
 import numpy as np
 
+from . import settings
 from .accumulate import PI_MAX
 from .coverage import (FINE_HALF_KM, FINE_STEP_KM, META, OUT, TIMES, best_calibrated,
                        gather_calibrated, log, truth_cell)
@@ -326,6 +322,7 @@ def figure():
 
 
 if __name__ == "__main__":
+    settings.default_profile("calibrated")   # configs/calibrated.toml unless FIGLIB_PROFILE is set
     import sys
     if sys.argv[1:] == ["figure"]:
         print(figure())
diff --git a/src/figlib/corpus.py b/src/figlib/corpus.py
index 539aea0..b8b1275 100644
--- a/src/figlib/corpus.py
+++ b/src/figlib/corpus.py
@@ -26,6 +26,8 @@
 from dataclasses import dataclass
 from pathlib import Path
 
+from . import settings
+
 ROOT = Path(__file__).resolve().parents[2]
 SHARED_META = ROOT / "data" / "meta"
 
@@ -65,7 +67,7 @@ class Corpus:
 
 
 def current() -> Corpus:
-    name = os.environ.get("FIGLIB_CORPUS", "core")
+    name = settings.get("FIGLIB_CORPUS")
     if name not in CORPORA:
         raise ValueError(f"FIGLIB_CORPUS={name!r}; expected one of {sorted(CORPORA)}")
     return CORPORA[name]
@@ -107,7 +109,7 @@ def contamination(name: str) -> str:
 
 def tier_filter() -> tuple[str, ...] | None:
     """Tiers named in FIGLIB_TIER, or None to score everything."""
-    raw = os.environ.get("FIGLIB_TIER")
+    raw = settings.get("FIGLIB_TIER")
     if not raw:
         return None
     tiers = tuple(t.strip() for t in raw.split(",") if t.strip())
diff --git a/src/figlib/coverage.py b/src/figlib/coverage.py
index 7fd15e2..e93aafd 100644
--- a/src/figlib/coverage.py
+++ b/src/figlib/coverage.py
@@ -18,17 +18,13 @@
 uniform on [0, 1]. Finally it finds the single log-likelihood temperature that would make
 the 95% region cover 95% -- how much wider the posterior needs to be.
 
-Uses the calibrated camera model (fisheye lens + star pose ledger), the current best.
+Uses the calibrated camera model (configs/calibrated.toml: fisheye lens + star pose ledger),
+the current best, unless FIGLIB_PROFILE names another.
 
     python -m src.figlib.coverage          # writes out/confidence/
 """
 from __future__ import annotations
 
-import os
-
-os.environ.setdefault("FIGLIB_LENS", "fisheye")
-os.environ.setdefault("FIGLIB_POSE_LEDGER", "1")
-
 import json
 import math
 import time
@@ -37,6 +33,7 @@
 import numpy as np
 
 from . import corpus as C
+from . import settings
 from . import pose_ledger
 from .accumulate import posterior as accum_posterior
 from .geolocate import FRAME_SIZES, YOLO_DIR, solve
@@ -184,7 +181,7 @@ def main() -> None:
     resolved = [r for r in json.loads((META / "resolved.json").read_text())
                 if r["tier"] in ("confirmed", "probable") and r.get("triangulable")]
     log(f"coverage: {len(resolved)} scoring fires x {len(TIMES)} times x 2 estimators, "
-        f"lens={os.environ['FIGLIB_LENS']} ledger={os.environ['FIGLIB_POSE_LEDGER']}")
+        f"lens={settings.get('FIGLIB_LENS')} ledger={settings.get('FIGLIB_POSE_LEDGER')}")
 
     rows, grids = [], []
     for k, r in enumerate(resolved, 1):
@@ -301,6 +298,7 @@ def figure() -> Path:
 
 
 if __name__ == "__main__":
+    settings.default_profile("calibrated")   # configs/calibrated.toml unless FIGLIB_PROFILE is set
     import sys
     if sys.argv[1:] == ["figure"]:
         print(figure())
diff --git a/src/figlib/falsealarm.py b/src/figlib/falsealarm.py
index e3c4c5f..fe3b737 100644
--- a/src/figlib/falsealarm.py
+++ b/src/figlib/falsealarm.py
@@ -30,7 +30,6 @@
 from __future__ import annotations
 
 import json
-import os
 import math
 from collections import defaultdict
 from pathlib import Path
@@ -38,6 +37,7 @@
 import numpy as np
 
 from . import corpus as C
+from . import settings
 from .geom import offset_bearing_deg
 
 
@@ -60,7 +60,7 @@ def poisson_hi(n: int, cam_days: float) -> float | None:
 # Which detection pass to score. Overridable so the Core ML variants run through this
 # exact pipeline rather than a parallel one -- the point of the quantization study is a
 # paired comparison, and a second implementation would be a second source of difference.
-YOLO_DIR = Path(os.environ.get("FIGLIB_DETS", CORPUS.dets))
+YOLO_DIR = Path(settings.get("FIGLIB_DETS") or CORPUS.dets)
 OUT = CORPUS.out
 # Restrict scoring to what the detector cannot have trained on (corpus.contamination).
 TIERS = C.tier_filter()
diff --git a/src/figlib/fig_bearing.py b/src/figlib/fig_bearing.py
index 1cffc82..7dec43e 100644
--- a/src/figlib/fig_bearing.py
+++ b/src/figlib/fig_bearing.py
@@ -27,12 +27,6 @@
 
 from __future__ import annotations
 
-import os
-
-# The calibrated camera model, as in coverage.py. Set before the geometry reads them.
-os.environ.setdefault("FIGLIB_LENS", "fisheye")
-os.environ.setdefault("FIGLIB_POSE_LEDGER", "1")
-
 import json
 import math
 from pathlib import Path
@@ -40,6 +34,7 @@
 import numpy as np
 
 from . import corpus as C
+from . import settings
 from . import provenance as P
 from . import terrain_range as TR
 from .fig_triangulate import PALETTE, _det_for, _hillshade, _plume_crop, _title
@@ -425,6 +420,7 @@ def sheet(tile_w: int = 560, cols: int = 4) -> Path:
 
 
 if __name__ == "__main__":
+    settings.default_profile("calibrated")   # configs/calibrated.toml unless FIGLIB_PROFILE is set
     import sys
     a = sys.argv[1:]
     if a and a[0] == "sheet":
diff --git a/src/figlib/geolocate.py b/src/figlib/geolocate.py
index 7cdea1c..ff222f8 100644
--- a/src/figlib/geolocate.py
+++ b/src/figlib/geolocate.py
@@ -22,7 +22,6 @@
 from __future__ import annotations
 
 import json
-import os
 import math
 from dataclasses import dataclass
 from pathlib import Path
@@ -32,6 +31,7 @@
 from .geom import (angdiff_deg, bearing_deg, bearing_grid, haversine_km, load_cams,
                     offset_bearing_deg)
 from . import corpus as C
+from . import settings
 from . import pose_ledger
 from .wind import upwind_x, wind_at
 
@@ -40,7 +40,7 @@
 # Which detection pass to score. Overridable so the Core ML variants run through this
 # exact pipeline rather than a parallel one -- the point of the quantization study is a
 # paired comparison, and a second implementation would be a second source of difference.
-YOLO_DIR = Path(os.environ.get("FIGLIB_DETS", C.current().dets))
+YOLO_DIR = Path(settings.get("FIGLIB_DETS") or C.current().dets)
 
 # Frame width per archive (frame_sizes.py). The star-measured fisheye lens and the pose
 # ledger apply only to the frame format they were measured on, and the same camera name
@@ -53,9 +53,14 @@
 # source. Two degrees is deliberately generous: claiming less would shrink the
 # uncertainty region without earning it.
 SIGMA_DEG = 2.0
-# Opt-in: re-find the peak at this step within two cells of the grid's (`refine`). Off by
-# default so every published number stays the grid's, until they are re-run together.
-REFINE_KM = float(os.environ["FIGLIB_REFINE_KM"]) if os.environ.get("FIGLIB_REFINE_KM") else None
+
+
+def refine_step() -> float | None:
+    """FIGLIB_REFINE_KM: the step at which `solve` re-finds its peak within two cells of the
+    grid's (`refine`), 0.01 km unless set; None when set to 0, for the bare grid node."""
+    step = float(settings.get("FIGLIB_REFINE_KM") or 0)
+    return step if step > 0 else None
+
 
 # Scored columns, in the order they are reported. The four box variants run by
 # default; everything past them takes the bearing from a pixel mask instead of a box
@@ -67,8 +72,8 @@
                  "wedge_diff", "wedge_sam", "seq_diff", "seq_sam",
                  "field_diff", "field_sam")
 ALL_VARIANTS = BOX_VARIANTS + MASK_VARIANTS
-if os.environ.get("FIGLIB_VARIANTS"):
-    _keep = set(os.environ["FIGLIB_VARIANTS"].split(","))
+if settings.get("FIGLIB_VARIANTS"):
+    _keep = set(settings.get("FIGLIB_VARIANTS").split(","))
     VARIANTS = tuple(v for v in ALL_VARIANTS if v in _keep)
 else:
     VARIANTS = BOX_VARIANTS
@@ -203,7 +208,7 @@ def _fit_x(kind: str, method: str, seq_name: str, offset: int, det: dict,
     # two thirds of the sequences where the crosswind is decisive -- so it is not
     # obvious the constraint earns its rejections. FIGLIB_WIND_GATE=0 turns it off, to
     # measure that rather than argue about it.
-    if os.environ.get("FIGLIB_WIND_GATE") == "0":
+    if settings.get("FIGLIB_WIND_GATE") == "0":
         cross = None
 
     if kind == "seq":
@@ -230,13 +235,14 @@ def _fit_x(kind: str, method: str, seq_name: str, offset: int, det: dict,
 
 def solve(bearings: list[Bearing], center: tuple[float, float],
           half_extent_km: float = 60.0, step_km: float = 0.4,
-          sigma_deg: float = SIGMA_DEG, refine_km: float | None = REFINE_KM):
+          sigma_deg: float = SIGMA_DEG, refine_km: float | None = None):
     """Log-likelihood surface over the ground, and its peak.
 
     The surface is returned at `step_km`. Without refinement the peak can only be a grid
     node. A 0.4 km cell then moves single fires by up to 0.5 km as the grid shifts under
     them, and can carry a median across the 2 km line (NOTES.md, 2026-09-25).
-    `refine_km` (FIGLIB_REFINE_KM) re-finds the peak off the grid; see `refine`."""
+    `refine_km` re-finds the peak off the grid (see `refine`): None takes FIGLIB_REFINE_KM
+    (`refine_step`), and 0 keeps the grid node."""
     lat0, lon0 = center
     dlat = step_km / 111.32
     dlon = step_km / (111.32 * math.cos(math.radians(lat0)))
@@ -261,8 +267,9 @@ def solve(bearings: list[Bearing], center: tuple[float, float],
 
     i, j = np.unravel_index(np.argmax(total), total.shape)
     la, lo = float(lats[i]), float(lons[j])
-    if refine_km:
-        la, lo = refine(bearings, la, lo, step_km, sigma_deg=sigma_deg, fine_km=refine_km)
+    fine_km = refine_step() if refine_km is None else refine_km
+    if fine_km:
+        la, lo = refine(bearings, la, lo, step_km, sigma_deg=sigma_deg, fine_km=fine_km)
     return lats, lons, total, la, lo
 
 
@@ -273,9 +280,9 @@ def refine(bearings: list[Bearing], lat: float, lon: float, step_km: float,
     `extra(lats, lons)`, if given, adds a term on the fine grid, as it was added on the
     coarse one (terrain_range's penalty). The fine grid is centred on the coarse peak, so it
     moves with the peak and is not tied to the coarse grid's origin."""
-    fine_km = fine_km or REFINE_KM or 0.01
+    fine_km = fine_km or refine_step() or 0.01
     lats, lons, ll, la, lo = solve(bearings, (lat, lon), half_extent_km=2 * step_km,
-                                   step_km=fine_km, sigma_deg=sigma_deg, refine_km=None)
+                                   step_km=fine_km, sigma_deg=sigma_deg, refine_km=0)
     if extra is not None:
         ll = ll + extra(lats, lons)
         i, j = np.unravel_index(np.argmax(ll), ll.shape)
@@ -355,7 +362,7 @@ def main() -> None:
     _save_wind(wind_cache)
 
     # Calibration variants write beside the baseline, never over it.
-    variant = (("_fisheye" if os.environ.get("FIGLIB_LENS") == "fisheye" else "")
+    variant = (("_fisheye" if settings.get("FIGLIB_LENS") == "fisheye" else "")
                + ("_ledger" if pose_ledger.enabled() else "")
                + ("_full" if pose_ledger.enabled() and pose_ledger.full_enabled() else ""))
     dest = C.current().out / f"geolocation{C.tier_suffix(tiers)}{variant}.json"
@@ -364,7 +371,7 @@ def main() -> None:
     from .wind import CACHE as WIND_CACHE
     P.record("geolocate", [dest, WIND_CACHE], started=started,
              params={"variants": list(VARIANTS), "sigma_deg": SIGMA_DEG,
-                     "lens": os.environ.get("FIGLIB_LENS", "rectilinear"),
+                     "lens": settings.get("FIGLIB_LENS"),
                      "pose_ledger": str(pose_ledger.path()) if pose_ledger.enabled() else None,
                      "pose_full": pose_ledger.enabled() and pose_ledger.full_enabled()},
              extra_inputs=[META / "sequences.json", META / "fires.json",
diff --git a/src/figlib/geom.py b/src/figlib/geom.py
index e280cba..f61e42d 100644
--- a/src/figlib/geom.py
+++ b/src/figlib/geom.py
@@ -18,6 +18,8 @@
 import math
 from pathlib import Path
 
+from . import settings
+
 EARTH_R_KM = 6371.0
 
 
@@ -27,8 +29,7 @@ def load_cams() -> dict:
     Set FIGLIB_CAMS to `data/meta/cams_refined.json` to run everything downstream against
     terrain-fitted azimuths instead of the published ones.
     """
-    import os
-    p = os.environ.get("FIGLIB_CAMS")
+    p = settings.get("FIGLIB_CAMS")
     root = Path(__file__).resolve().parents[2]
     return json.loads(Path(p if p else root / "data" / "meta" / "cams.json").read_text())
 
@@ -186,8 +187,7 @@ def _fisheye(cam: dict) -> bool:
     Callers that know the frame width pass it as cam["frame_w"] (see frame_sizes.py);
     without it the lens is not applied.
     """
-    import os
-    return (os.environ.get("FIGLIB_LENS") == "fisheye" and cam.get("fov") == 90
+    return (settings.get("FIGLIB_LENS") == "fisheye" and cam.get("fov") == 90
             and cam.get("frame_w") == 3072)
 
 
diff --git a/src/figlib/pose_ledger.py b/src/figlib/pose_ledger.py
index f52a642..3a0511e 100644
--- a/src/figlib/pose_ledger.py
+++ b/src/figlib/pose_ledger.py
@@ -34,10 +34,11 @@
 from __future__ import annotations
 
 import json
-import os
 import statistics
 from pathlib import Path
 
+from . import settings
+
 ROOT = Path(__file__).resolve().parents[2]
 LEDGER = ROOT / "data" / "meta" / "pose_ledger.json"
 SAME_DAYS = 3.0
@@ -49,17 +50,17 @@
 
 
 def enabled() -> bool:
-    return os.environ.get("FIGLIB_POSE_LEDGER") == "1"
+    return settings.flag("FIGLIB_POSE_LEDGER")
 
 
 def full_enabled() -> bool:
     """FIGLIB_POSE_FULL=1: bearings go through the whole solved camera -- its own lens, pitch
     and roll, read at the box's foot -- instead of the shared lens along the middle row."""
-    return os.environ.get("FIGLIB_POSE_FULL") == "1"
+    return settings.flag("FIGLIB_POSE_FULL")
 
 
 def path() -> Path:
-    return Path(os.environ.get("FIGLIB_POSE_LEDGER_PATH") or LEDGER)
+    return Path(settings.get("FIGLIB_POSE_LEDGER_PATH") or LEDGER)
 
 
 LEGACY_MODEL = "legacy: J2000, uncorrected"
diff --git a/src/figlib/provenance.py b/src/figlib/provenance.py
index 45c034b..778d6b6 100644
--- a/src/figlib/provenance.py
+++ b/src/figlib/provenance.py
@@ -9,7 +9,8 @@
   258-byte placeholder with no frames -- so the hash is what pins a result to its input.
 * **Run log** (`<corpus metadata>/runs.jsonl`). One line appended per pipeline stage:
   git commit and a hash of any uncommitted diff, package versions, the model pin, input
-  manifest hashes, parameters, and the SHA-256 of every output. A result file whose hash
+  manifest hashes, parameters, every FIGLIB_* setting as resolved (`settings.py`, with the
+  profile's hash), and the SHA-256 of every output. A result file whose hash
   is not in the log did not come from a recorded run.
 
 The detector weights are pinned here, and `check_model` refuses to run on anything else.
@@ -31,6 +32,7 @@
 from pathlib import Path
 
 from . import corpus as C
+from . import settings
 
 ROOT = C.ROOT
 MANIFESTS = C.SHARED_META / "manifests"
@@ -77,7 +79,7 @@ def check_model(path: Path | None = None) -> dict:
     path = path or ROOT / MODEL["file"]
     actual = sha256_file(path)
     if actual != MODEL["sha256"]:
-        if os.environ.get("FIGLIB_ALLOW_UNPINNED_MODEL") != "1":
+        if not settings.flag("FIGLIB_ALLOW_UNPINNED_MODEL"):
             raise RuntimeError(
                 f"{rel(path)} has sha256 {actual}, not the pinned {MODEL['sha256']} "
                 f"({MODEL['source']} @ {MODEL['revision'][:8]}). Refetch it per "
@@ -97,8 +99,9 @@ def _git(*args: str) -> str | None:
 
 
 def code_state() -> dict:
-    diff = _git("diff", "HEAD", "--", "src", "tests", "run_detect.sh", "data/fetch.sh")
-    untracked = _git("ls-files", "--others", "--exclude-standard", "--", "src")
+    diff = _git("diff", "HEAD", "--", "src", "tests", "configs", "run_detect.sh",
+                "data/fetch.sh")
+    untracked = _git("ls-files", "--others", "--exclude-standard", "--", "src", "configs")
     return {
         "commit": (_git("rev-parse", "HEAD") or "").strip() or None,
         "branch": (_git("rev-parse", "--abbrev-ref", "HEAD") or "").strip() or None,
@@ -152,6 +155,8 @@ def record(stage: str, outputs, params: dict | None = None, model: dict | None =
         "finished_utc": utc_now(),
         "argv": sys.argv,
         "env": {k: v for k, v in sorted(os.environ.items()) if k.startswith("FIGLIB_")},
+        # Every FIGLIB_* setting as the run saw it, defaults included, and the profile's hash.
+        "settings": settings.resolved(),
         "code": code_state(),
         "versions": _versions(),
         "platform": platform.platform(),
diff --git a/src/figlib/recent.py b/src/figlib/recent.py
index 6debae9..b263c32 100644
--- a/src/figlib/recent.py
+++ b/src/figlib/recent.py
@@ -48,6 +48,7 @@
 from zoneinfo import ZoneInfo
 
 from . import corpus as C
+from . import settings
 
 ROOT = Path(__file__).resolve().parents[2]
 FRAMES = ROOT / "data" / "hpwren_recent"
@@ -378,7 +379,7 @@ def score() -> Path:
             row[tag] = out
         rows.append(row)
 
-    variant = (("_fisheye" if os.environ.get("FIGLIB_LENS") == "fisheye" else "")
+    variant = (("_fisheye" if settings.get("FIGLIB_LENS") == "fisheye" else "")
                + ("_ledger" if pose_ledger.enabled() else "")
                + ("_full" if pose_ledger.enabled() and pose_ledger.full_enabled() else ""))
     dest = CORPUS.out / f"geolocation{variant}.json"
@@ -386,7 +387,7 @@ def score() -> Path:
     dest.write_text(json.dumps(rows, indent=1) + "\n")
     P.record("recent-score", [dest], started=started,
              params={"variants": [v[0] for v in VARIANTS], "use_wind": False,
-                     "lens": os.environ.get("FIGLIB_LENS", "rectilinear"),
+                     "lens": settings.get("FIGLIB_LENS"),
                      "pose_ledger": str(pose_ledger.path()) if pose_ledger.enabled() else None,
                      "pose_full": pose_ledger.enabled() and pose_ledger.full_enabled()},
              extra_inputs=[meta / "sequences.json", meta / "fires.json", meta / "resolved.json",
diff --git a/src/figlib/settings.py b/src/figlib/settings.py
new file mode 100644
index 0000000..3877d95
--- /dev/null
+++ b/src/figlib/settings.py
@@ -0,0 +1,122 @@
+"""Every FIGLIB_* switch in one place: its default, and where its value came from.
+
+A value comes from, in order:
+
+1. the environment (`FIGLIB_LENS=fisheye python -m ...`);
+2. a named profile, a committed file `configs/<name>.toml` selected with `FIGLIB_PROFILE`;
+3. a module's own default profile, named by that module's `main()` (`default_profile`);
+4. the default below.
+
+Values are read when they are asked for, never written back into `os.environ`, so importing
+a module cannot change what another one sees. The run log (`provenance.record`) stores the
+whole resolved set, defaults included, with the profile's name and hash.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import os
+from pathlib import Path
+
+import tomllib
+
+ROOT = Path(__file__).resolve().parents[2]
+CONFIGS = ROOT / "configs"
+
+# name -> (default, what it does). None means unset.
+SETTINGS: dict[str, tuple[str | None, str]] = {
+    "FIGLIB_CORPUS": ("core", "which archives and outputs: core, extra, all, recent"),
+    "FIGLIB_TIER": (None, "restrict scoring to contamination tiers, comma-separated"),
+    "FIGLIB_DETS": (None, "alternative detection directory (default: the corpus's own)"),
+    "FIGLIB_CAMS": (None, "alternative camera table (default: data/meta/cams.json)"),
+    "FIGLIB_LENS": ("rectilinear", "rectilinear, or fisheye for the star-measured lens"),
+    "FIGLIB_POSE_LEDGER": ("0", "1: per-camera star azimuths from the pose ledger"),
+    "FIGLIB_POSE_LEDGER_PATH": (None, "alternative pose ledger (default: data/meta/pose_ledger.json)"),
+    "FIGLIB_POSE_FULL": ("0", "1: bearings through the whole solved camera (lens, pitch, roll)"),
+    "FIGLIB_REFINE_KM": ("0.01", "re-find the likelihood peak at this step off the grid; 0 for grid nodes"),
+    "FIGLIB_VARIANTS": (None, "bearing variants to score, comma-separated (default: the box variants)"),
+    "FIGLIB_WIND_GATE": ("1", "0: mask fits may lean against the wind"),
+    "FIGLIB_BAND_PX": ("100", "terrain_range: allowed rows above the terrain line"),
+    "FIGLIB_ALLOW_UNPINNED_MODEL": ("0", "1: run detection on weights other than the pinned ones"),
+}
+
+_module_default: str | None = None
+
+
+def default_profile(name: str) -> None:
+    """The profile a module's `main()` runs under when FIGLIB_PROFILE is not set.
+
+    Call it from `main()`, never at import: it applies to the whole process."""
+    global _module_default
+    _module_default = name
+
+
+def _profile() -> tuple[str, str, dict[str, str]] | None:
+    """(name, source, values) of the active profile, or None."""
+    name, source = os.environ.get("FIGLIB_PROFILE") or None, "env"
+    if name is None and _module_default:
+        name, source = _module_default, "module default"
+    if name is None:
+        return None
+    return name, source, profile_values(name)
+
+
+def profile_path(name: str) -> Path:
+    p = CONFIGS / f"{name}.toml"
+    if not p.exists():
+        have = sorted(q.stem for q in CONFIGS.glob("*.toml"))
+        raise ValueError(f"FIGLIB_PROFILE={name!r}: no {p}; profiles are {have}")
+    return p
+
+
+def profile_values(name: str) -> dict[str, str]:
+    raw = tomllib.loads(profile_path(name).read_text())
+    bad = sorted(set(raw) - set(SETTINGS))
+    if bad:
+        raise ValueError(f"configs/{name}.toml sets unknown {bad}; known are {sorted(SETTINGS)}")
+    return {k: ("1" if v is True else "0" if v is False else str(v)) for k, v in raw.items()}
+
+
+def get(name: str) -> str | None:
+    """The value of one FIGLIB_* setting: environment, then profile, then default."""
+    if name not in SETTINGS:
+        raise KeyError(f"{name} is not a setting; add it to settings.SETTINGS")
+    v = os.environ.get(name)
+    if v:
+        return v
+    prof = _profile()
+    if prof and name in prof[2]:
+        return prof[2][name]
+    return SETTINGS[name][0]
+
+
+def flag(name: str) -> bool:
+    return get(name) == "1"
+
+
+def resolved() -> dict:
+    """Every setting's value and its source, and the profile: what the run log stores."""
+    prof = _profile()
+    values, source = {}, {}
+    for name, (default, _) in SETTINGS.items():
+        values[name] = get(name)
+        source[name] = ("env" if os.environ.get(name) else
+                        "profile" if prof and name in prof[2] else "default")
+    out = {"values": values, "source": source, "profile": None}
+    if prof:
+        p = profile_path(prof[0])
+        out["profile"] = {"name": prof[0], "selected_by": prof[1], "file": str(p.relative_to(ROOT)),
+                          "sha256": hashlib.sha256(p.read_bytes()).hexdigest()}
+    return out
+
+
+def main() -> None:
+    r = resolved()
+    if r["profile"]:
+        print(f"profile {r['profile']['name']} ({r['profile']['file']}, {r['profile']['sha256'][:12]})")
+    for name, (default, doc) in SETTINGS.items():
+        print(f"  {name:28s} {str(r['values'][name]):12s} {r['source'][name]:8s} {doc}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/figlib/terrain_range.py b/src/figlib/terrain_range.py
index fcb4770..dbbf31f 100644
--- a/src/figlib/terrain_range.py
+++ b/src/figlib/terrain_range.py
@@ -26,12 +26,6 @@
 
 from __future__ import annotations
 
-import os
-
-# The calibrated camera model, as in coverage.py. Set before the geometry reads them.
-os.environ.setdefault("FIGLIB_LENS", "fisheye")
-os.environ.setdefault("FIGLIB_POSE_LEDGER", "1")
-
 import json
 import math
 import sys
@@ -39,12 +33,13 @@
 import numpy as np
 
 from . import corpus as C
+from . import settings
 from . import pose_ledger
 from . import provenance as P
 from . import terrain as T
 from .fig_triangulate import _det_for
-from .geolocate import (FRAME_SIZES, META, REFINE_KM, YOLO_DIR, bearings_for_fire, credible_area_km2,
-                        refine, solve)
+from .geolocate import (FRAME_SIZES, META, YOLO_DIR, bearings_for_fire, credible_area_km2,
+                        refine, refine_step, solve)
 from .geom import angdiff_deg, bearing_deg, enu_grid, haversine_km, load_cams, ray_latlon
 from .stars.fisheye import initial_k, project_fisheye
 
@@ -57,7 +52,7 @@
 # The near-side bound: the terrain line's row may lie at most this far below the early box
 # bottom. 100 px lost no truth the cap kept on 73 validated bearings (validate, 2026-09-14);
 # narrower bands start dropping truths.
-BAND_PX = float(os.environ.get("FIGLIB_BAND_PX", 100))
+BAND_PX = float(settings.get("FIGLIB_BAND_PX"))
 BAND_GRID = (30, 60, 100, 150, 200, 300)
 STEP_KM = 0.4                   # geolocate.solve's default grid, which the penalty is added on
 NEAR_SAMPLES = 10       # truth is "contained" within this many samples (0.3 km): the official
@@ -329,7 +324,7 @@ def solve_compare(band_px: float | None = BAND_PX, ledger_only: bool = False) ->
             ll2 = ll + terrain_loglik(lats, lons, terr, band_px)
             i, j = np.unravel_index(np.argmax(ll2), ll2.shape)
             alat, alon = float(lats[i]), float(lons[j])
-            if REFINE_KM:
+            if refine_step():
                 alat, alon = refine(bs, alat, alon, STEP_KM,
                                     extra=lambda la, lo: terrain_loglik(la, lo, terr, band_px))
             rec["after_km"] = round(haversine_km(alat, alon, t["lat"], t["lon"]), 2)
@@ -387,7 +382,7 @@ def before_after(fire_id: str, band_px: float = BAND_PX):
     ll1 = ll + terrain_loglik(lats, lons, terr, band_px)
     i, j = np.unravel_index(np.argmax(ll1), ll1.shape)
     e1lat, e1lon = float(lats[i]), float(lons[j])
-    if REFINE_KM:
+    if refine_step():
         e1lat, e1lon = refine(bs, e1lat, e1lon, STEP_KM,
                               extra=lambda la, lo: terrain_loglik(la, lo, terr, band_px))
     err1 = haversine_km(e1lat, e1lon, t["lat"], t["lon"]); area1 = credible_area_km2(lats, lons, ll1)
@@ -563,6 +558,7 @@ def draw_map(ax, surface, est, err, area, title, with_terrain):
 
 
 if __name__ == "__main__":
+    settings.default_profile("calibrated")   # configs/calibrated.toml unless FIGLIB_PROFILE is set
     a = sys.argv[1:]
     if a and a[0] == "validate":
         validate()
diff --git a/tests/test_geolocation.py b/tests/test_geolocation.py
index 4b5e2fd..822dc66 100644
--- a/tests/test_geolocation.py
+++ b/tests/test_geolocation.py
@@ -21,8 +21,8 @@
 def calibrated(monkeypatch):
     """The calibrated camera model the corpus figures use: star-measured fisheye and ledger poses.
 
-    Pinned here rather than inherited: coverage, bias, fig_bearing and terrain_range set these
-    at import, so without the pin the result depended on which test module was collected first.
+    Pinned here: configs/calibrated.toml is only the default of those modules' `main()`, so an
+    imported module scores whatever the environment says.
     """
     monkeypatch.setenv("FIGLIB_LENS", "fisheye")
     monkeypatch.setenv("FIGLIB_POSE_LEDGER", "1")
diff --git a/tests/test_settings.py b/tests/test_settings.py
new file mode 100644
index 0000000..44bfb75
--- /dev/null
+++ b/tests/test_settings.py
@@ -0,0 +1,86 @@
+"""The FIGLIB_* settings: environment over profile over default, and nothing set at import.
+
+Before settings.py, four modules ran `os.environ.setdefault` when imported, so a result
+depended on which of them had been imported first (NOTES.md, 2026-09-25).
+"""
+
+from __future__ import annotations
+
+import importlib
+import os
+
+import pytest
+
+from src.figlib import settings as S
+
+
+@pytest.fixture(autouse=True)
+def clean(monkeypatch):
+    for k in list(os.environ):
+        if k.startswith("FIGLIB_"):
+            monkeypatch.delenv(k)
+    monkeypatch.setattr(S, "_module_default", None)
+
+
+def test_defaults_with_nothing_set():
+    assert S.get("FIGLIB_CORPUS") == "core"
+    assert S.get("FIGLIB_LENS") == "rectilinear"
+    assert not S.flag("FIGLIB_POSE_LEDGER")
+    assert S.get("FIGLIB_DETS") is None
+    r = S.resolved()
+    assert r["profile"] is None
+    assert set(r["values"]) == set(S.SETTINGS)
+    assert set(r["source"].values()) == {"default"}
+
+
+def test_profile_then_environment(monkeypatch):
+    monkeypatch.setenv("FIGLIB_PROFILE", "calibrated")
+    assert S.get("FIGLIB_LENS") == "fisheye" and S.flag("FIGLIB_POSE_LEDGER")
+    monkeypatch.setenv("FIGLIB_LENS", "rectilinear")
+    r = S.resolved()
+    assert r["values"]["FIGLIB_LENS"] == "rectilinear" and r["source"]["FIGLIB_LENS"] == "env"
+    assert r["source"]["FIGLIB_POSE_LEDGER"] == "profile"
+    assert r["profile"]["name"] == "calibrated" and r["profile"]["selected_by"] == "env"
+    assert len(r["profile"]["sha256"]) == 64
+
+
+def test_module_default_yields_to_named_profile(monkeypatch):
+    S.default_profile("calibrated")
+    assert S.get("FIGLIB_LENS") == "fisheye"
+    assert S.resolved()["profile"]["selected_by"] == "module default"
+    monkeypatch.setenv("FIGLIB_PROFILE", "recent")
+    assert S.get("FIGLIB_POSE_LEDGER_PATH") == "out/recent/pose_ledger.json"
+
+
+def test_bad_profiles_fail_loudly(monkeypatch, tmp_path):
+    monkeypatch.setenv("FIGLIB_PROFILE", "no-such-profile")
+    with pytest.raises(ValueError, match="no-such-profile"):
+        S.get("FIGLIB_LENS")
+    (tmp_path / "typo.toml").write_text('FIGLIB_LENZ = "fisheye"\n')
+    monkeypatch.setattr(S, "CONFIGS", tmp_path)
+    monkeypatch.setenv("FIGLIB_PROFILE", "typo")
+    with pytest.raises(ValueError, match="FIGLIB_LENZ"):
+        S.get("FIGLIB_LENS")
+    with pytest.raises(KeyError):
+        S.get("FIGLIB_LENZ")
+
+
+def test_every_profile_parses():
+    for p in S.CONFIGS.glob("*.toml"):
+        assert S.profile_values(p.stem)
+
+
+@pytest.mark.parametrize("name", ["coverage", "bias", "fig_bearing", "terrain_range"])
+def test_importing_sets_nothing(name):
+    importlib.reload(importlib.import_module(f"src.figlib.{name}"))
+    assert not [k for k in os.environ if k.startswith("FIGLIB_")]
+    assert S.get("FIGLIB_LENS") == "rectilinear"
+
+
+def test_refinement_is_on_unless_set_to_zero(monkeypatch):
+    from src.figlib.geolocate import refine_step
+    assert refine_step() == 0.01
+    monkeypatch.setenv("FIGLIB_REFINE_KM", "0")
+    assert refine_step() is None
+    monkeypatch.setenv("FIGLIB_REFINE_KM", "0.05")
+    assert refine_step() == 0.05

From f7eabac3fe2f45e43435621c0d143381cc69ed30 Mon Sep 17 00:00:00 2001
From: Roy Harnish <royharnish@gmail.com>
Date: Sat, 26 Sep 2026 11:27:42 -0700
Subject: [PATCH 2/4] Calibrated profiles take the whole solved camera
 (FIGLIB_POSE_FULL)

Measured before deciding, with refinement on. Core corpus: bearings move a median 0.00 deg
and at most 0.30 (sm-n at Steele); seven fires move by 0.03 km or less; no count changes.
Recent corpus (NOTES, 2026-09-25): it removes the bm-lens miss (bm-w at Bernardo +4.6 ->
+1.6 deg, bm-e at Junction +4.6 -> +1.8), and Bernardo loses the 0.63 km that miss had
bought it. The model is the one the stars measured, so it is the calibrated one.

compare_geolocation gains the _full column; stars.fig_geolocation draws it as "after".

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 configs/calibrated.toml             |  6 ++++--
 configs/recent.toml                 |  1 +
 src/figlib/compare_geolocation.py   | 11 ++++++-----
 src/figlib/recent.py                |  2 +-
 src/figlib/stars/fig_geolocation.py |  7 ++++---
 5 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/configs/calibrated.toml b/configs/calibrated.toml
index 9f4e9cd..32d1e27 100644
--- a/configs/calibrated.toml
+++ b/configs/calibrated.toml
@@ -1,5 +1,7 @@
-# The calibrated camera model: the star-measured fisheye lens and per-camera star poses
-# from data/meta/pose_ledger.json. coverage, bias, fig_bearing and terrain_range run under
+# The calibrated camera model: the star-measured fisheye lens, and each camera's star solve
+# from data/meta/pose_ledger.json -- its azimuth, and (FIGLIB_POSE_FULL) its own lens, pitch
+# and roll, read at the box's foot. coverage, bias, fig_bearing and terrain_range run under
 # this profile unless FIGLIB_PROFILE names another; anything set in the environment wins.
 FIGLIB_LENS = "fisheye"
 FIGLIB_POSE_LEDGER = "1"
+FIGLIB_POSE_FULL = "1"
diff --git a/configs/recent.toml b/configs/recent.toml
index 84f79ed..567f5ab 100644
--- a/configs/recent.toml
+++ b/configs/recent.toml
@@ -2,4 +2,5 @@
 # pose ledger, which adds the star solves of cameras FIgLib never annotated.
 FIGLIB_LENS = "fisheye"
 FIGLIB_POSE_LEDGER = "1"
+FIGLIB_POSE_FULL = "1"
 FIGLIB_POSE_LEDGER_PATH = "out/recent/pose_ledger.json"
diff --git a/src/figlib/compare_geolocation.py b/src/figlib/compare_geolocation.py
index 7fb69e2..00e15f4 100644
--- a/src/figlib/compare_geolocation.py
+++ b/src/figlib/compare_geolocation.py
@@ -1,11 +1,12 @@
 """Compare geolocation outputs across calibration variants, fire by fire.
 
 Each variant is a file geolocate.py writes: the baseline `geolocation.json`, and beside it
-`geolocation_fisheye.json` (FIGLIB_LENS=fisheye) and `geolocation_fisheye_ledger.json`
-(plus FIGLIB_POSE_LEDGER=1). Same detections and solver in all of them, so differences are
-the calibration alone.
+`geolocation_fisheye.json` (FIGLIB_LENS=fisheye), `geolocation_fisheye_ledger.json` (plus
+FIGLIB_POSE_LEDGER=1: star azimuths) and `geolocation_fisheye_ledger_full.json` (plus
+FIGLIB_POSE_FULL=1: the whole solved camera; FIGLIB_PROFILE=calibrated). Same detections
+and solver in all of them, so differences are the calibration alone.
 
-    python -m src.figlib.compare_geolocation [variant ...]    # default: all three, center bearing
+    python -m src.figlib.compare_geolocation [variant ...]    # default: all four, center bearing
 """
 
 from __future__ import annotations
@@ -16,7 +17,7 @@
 
 from . import corpus as C
 
-VARIANTS = ["", "_fisheye", "_fisheye_ledger"]
+VARIANTS = ["", "_fisheye", "_fisheye_ledger", "_fisheye_ledger_full"]
 
 
 def load(variant: str) -> dict[str, dict] | None:
diff --git a/src/figlib/recent.py b/src/figlib/recent.py
index b263c32..adc032c 100644
--- a/src/figlib/recent.py
+++ b/src/figlib/recent.py
@@ -19,7 +19,7 @@
     data/hpwren_recent/<fire_id>/<cam>/*.jpg        frames (gitignored)
     data/meta/recent/manifest.json                  source URL, SHA-256, Last-Modified per frame
     out/recent/yolo/<fire_id>_<cam>.json            detections, in detect_yolo's shape
-    out/recent/geolocation{_fisheye}{_ledger}.json  scores
+    out/recent/geolocation{_fisheye}{_ledger}{_full}.json  scores
 
 Fires, sequences and ground truth come from the `all` corpus, so no FIgLib result moves.
 Only the box-center bearings are scored (`center`, `early`), which need no wind lookup.
diff --git a/src/figlib/stars/fig_geolocation.py b/src/figlib/stars/fig_geolocation.py
index c85ff06..d2da41b 100644
--- a/src/figlib/stars/fig_geolocation.py
+++ b/src/figlib/stars/fig_geolocation.py
@@ -1,8 +1,9 @@
 """Geolocation before and after the star calibration, from geolocate.py's own outputs.
 
 Before: `geolocation.json` (published azimuth, rectilinear nameplate lens). After:
-`geolocation_fisheye_ledger.json` (FIGLIB_LENS=fisheye, FIGLIB_POSE_LEDGER=1). Same
-detections and solver, so every difference is the calibration.
+`geolocation_fisheye_ledger_full.json` (FIGLIB_PROFILE=calibrated: the star-measured lens and
+each camera's whole star solve). Same detections and solver, so every difference is the
+calibration.
 
   docs/figures/calibration_maps.png    bearing rays and estimates for a few fires
   docs/figures/calibration_bearings.png  every confirmed-fire bearing's miss vs frame position
@@ -146,7 +147,7 @@ def med(ps, i):
 
 
 def main(argv):
-    before, after = _load(""), _load("_fisheye_ledger")
+    before, after = _load(""), _load("_fisheye_ledger_full")
     cams = geom.load_cams()
     fires = argv
     if not fires:

From d469059e44fa9cfc4ba6542aae6d102f44038479 Mon Sep 17 00:00:00 2001
From: Roy Harnish <royharnish@gmail.com>
Date: Sat, 26 Sep 2026 11:29:01 -0700
Subject: [PATCH 3/4] rerun_results.sh: every stage that reaches the README,
 from one commit

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 rerun_results.sh | 80 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100755 rerun_results.sh

diff --git a/rerun_results.sh b/rerun_results.sh
new file mode 100755
index 0000000..8cb6f61
--- /dev/null
+++ b/rerun_results.sh
@@ -0,0 +1,80 @@
+#!/usr/bin/env bash
+# Every stage whose output reaches README.md or docs/, in dependency order, from one commit.
+#
+# Each stage appends a line to data/meta/{,all/,recent/}runs.jsonl with the commit, every
+# FIGLIB_* setting as resolved (settings.py) and the hash of every output, so run it from a
+# committed tree. Inputs are not re-made here: detections (run_detect.sh, `recent detect`),
+# star solves and the pose ledgers, CDN frames, DEM tiles.
+#
+#   ./rerun_results.sh            # ~20 min on four cores
+set -euo pipefail
+cd "$(dirname "$0")"
+PY=${PY:-.venv/bin/python}
+export OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1
+
+if ! git diff --quiet HEAD -- src tests configs; then
+  echo "uncommitted changes under src/, tests/ or configs/: commit first" >&2
+  exit 1
+fi
+mkdir -p out/logs
+LOG="out/logs/rerun_$(git rev-parse --short HEAD).log"
+exec > >(tee "$LOG") 2>&1
+m() { echo; echo "== $*"; "$PY" -m "$@"; }
+
+# Resize a figure onto docs/figures/ (JPEG at 1600 px wide, or a straight PNG copy).
+doc() {
+  "$PY" - "$1" "$2" <<'EOF'
+import sys
+from PIL import Image
+src, dst = sys.argv[1:]
+if dst.endswith(".png"):
+    Image.open(src).save(dst)
+else:
+    im = Image.open(src).convert("RGB")
+    im.resize((1600, round(im.height * 1600 / im.width)), Image.LANCZOS).save(dst, quality=85)
+print(f"{src} -> {dst}")
+EOF
+}
+
+# ---- core corpus: four camera models, same detections and solver
+m src.figlib.geolocate
+FIGLIB_LENS=fisheye m src.figlib.geolocate
+FIGLIB_PROFILE=calibrated FIGLIB_POSE_FULL=0 m src.figlib.geolocate    # star azimuths only
+FIGLIB_PROFILE=calibrated m src.figlib.geolocate                       # the whole solved camera
+m src.figlib.compare_geolocation
+m src.figlib.calfire
+m src.figlib.fig_triangulate 20171010_FIRE 20240701_Kitchenfire 20201202_WillowFire \
+  20171207_FIRE.2
+m src.figlib.animate_triangulate 20240701_Kitchenfire
+m src.figlib.fig_offsets
+m src.figlib.stars.fig_geolocation
+m src.figlib.evolve
+m src.figlib.coverage
+m src.figlib.bias
+
+doc out/triangulate/20171010_FIRE.png docs/figures/triangulate_portola.jpg
+doc out/triangulate/20201202_WillowFire-nightime-near-CDF-HQ.png docs/figures/triangulate_willow_night.jpg
+cp out/videos/triangulate_20240701_Kitchenfire.gif docs/figures/triangulate_kitchenfire.gif
+doc out/figures/offsets.png docs/figures/geolocation_offsets.png
+
+# ---- all of FIgLib: the calfire audit and the terrain range
+FIGLIB_CORPUS=all m src.figlib.geolocate
+FIGLIB_CORPUS=all m src.figlib.calfire
+export FIGLIB_CORPUS=all
+m src.figlib.terrain_range validate
+for band in cap 100 60 30; do m src.figlib.terrain_range solve "$band"; done
+m src.figlib.terrain_range solve 100 ledger
+m src.figlib.terrain_range figure 20260722_RainbowFire 30
+m src.figlib.terrain_range figure 20260722_RainbowFire 100
+m src.figlib.terrain_range figure 20240724_GroveFire 100
+m src.figlib.fig_bearing
+unset FIGLIB_CORPUS
+doc out/all/terrain_range/20260722_RainbowFire_terrain_band30.png docs/figures/terrain_range_rainbow.jpg
+
+# ---- recent: the extra CDN cameras (recent.py sets its own corpus)
+m src.figlib.recent score
+FIGLIB_LENS=fisheye m src.figlib.recent score
+FIGLIB_PROFILE=recent FIGLIB_POSE_FULL=0 m src.figlib.recent score
+FIGLIB_PROFILE=recent m src.figlib.recent score
+
+echo; echo "log: $LOG"

From 5495e1438552a9bae0a20de52bb77d614d2f4584 Mon Sep 17 00:00:00 2001
From: Roy Harnish <royharnish@gmail.com>
Date: Sat, 26 Sep 2026 12:38:33 -0700
Subject: [PATCH 4/4] The coordinated re-run: refined peaks and the whole
 solved camera, everywhere

Every stage in rerun_results.sh, from d469059 (the recent block run twice; the first four
entries were dirty from a docstring edit mid-run, and are kept). Confirmed-tier upper
medians, grid -> refined: 1.90 -> 1.87 (published), 1.70 -> 1.68 (fisheye),
2.00 -> 2.02 (star azimuth), 2.02 with the whole solved camera. All 26: 2.58 -> 2.80.
Rainbow 1.42 -> 2.17 and Palisades 1.82 -> 1.08 are refinement: their grid cells sat off
flat ridge peaks. README, figures, likelihood-over-time and NOTES follow the new numbers.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 NOTES.md                                  | 125 +++++++++++++++
 README.md                                 | 110 +++++++------
 data/README.md                            |   3 +-
 data/meta/all/calfire.json                | 186 +++++++++++-----------
 data/meta/all/runs.jsonl                  |  12 ++
 data/meta/calfire.json                    | 108 ++++++-------
 data/meta/recent/runs.jsonl               |   8 +
 data/meta/runs.jsonl                      |  10 ++
 docs/figures/calibration_bearings.png     | Bin 58001 -> 57861 bytes
 docs/figures/calibration_maps.png         | Bin 287906 -> 273170 bytes
 docs/figures/geolocation_offsets.png      | Bin 215185 -> 227953 bytes
 docs/figures/terrain_range_rainbow.jpg    | Bin 271618 -> 276378 bytes
 docs/figures/triangulate_kitchenfire.gif  | Bin 1045884 -> 1050306 bytes
 docs/figures/triangulate_portola.jpg      | Bin 220016 -> 208564 bytes
 docs/figures/triangulate_willow_night.jpg | Bin 248384 -> 201127 bytes
 docs/likelihood-over-time.md              |   6 +-
 tests/test_geolocation.py                 |   7 +-
 17 files changed, 375 insertions(+), 200 deletions(-)

diff --git a/NOTES.md b/NOTES.md
index 1503ad4..bdc72f7 100644
--- a/NOTES.md
+++ b/NOTES.md
@@ -2631,6 +2631,131 @@ table gives per-column |residual|, the median over cameras, and the mean |camera
 - **Skyline check:** "none" −4.17 / 6.39, "precession" +1.80 / 3.21, "all" +0.32 / 2.79.
   The same 35 of 39 cameras move closer to the ridge.
 
+## The coordinated re-run: refined peaks, the whole solved camera, one settings layer (2026-09-26)
+
+Every change that moves a README number was held for this one pass, so the numbers move once.
+Tagged `results-2026-09-26`.
+
+**What changed in the code.**
+- **`settings.py`.** All 13 FIGLIB_* switches are read in one place, with their defaults. A value
+  comes from the environment, then a profile (`configs/<name>.toml`, chosen with
+  FIGLIB_PROFILE or by a module's `main()`), then the default. Nothing is written into
+  `os.environ` any more. So the import-order bug from 2026-09-25 (terrain_range set
+  FIGLIB_LENS when imported, and test_geolocation failed when run alone) cannot come back:
+  coverage, bias, fig_bearing and terrain_range now select `calibrated` in `__main__`.
+  `tests/test_settings.py` checks that importing them sets nothing.
+- **The run log stores every setting as resolved,** defaults included, with where each value
+  came from and the profile's name and SHA-256. It used to store only the variables that were
+  set. `configs/` counts toward the dirty-tree check.
+- **FIGLIB_REFINE_KM defaults to 0.01.** `solve` re-finds its peak at 10 m within two cells
+  of the grid's best, unless set to 0. `accumulate.posterior` and `bias` still report their
+  own grid argmax; they are separate estimators and were not part of this change.
+- **FIGLIB_POSE_FULL is on in the calibrated profiles** (`calibrated`, `recent`). It was
+  measured first, with refinement on:
+  - Core corpus: bearings move a median 0.00° and at most 0.30° (sm-n at Steele). Seven fires
+    move by 0.03 km or less, and no count changes.
+  - Recent corpus: it removes the bm-lens miss (bm-w at Bernardo 249.70 → 246.72°, bm-e at
+    Junction 75.24 → 72.50°).
+  - The model is the camera the stars measured, so it is the calibrated one.
+    `compare_geolocation` gains the `_full` column, and `stars.fig_geolocation` draws it as
+    "after".
+- **`rerun_results.sh`** runs every stage that reaches the README, in order, and refuses a
+  dirty tree. All of this pass ran from d469059, in `out/logs/rerun_d469059{,_recent}.log`.
+  The recent block was run a second time because a test docstring was edited while it ran,
+  which marked the first four entries dirty. Those entries stay in the log. The rerun's
+  outputs are identical.
+
+**Core corpus, center bearing, upper medians** (grid → refined):
+
+| | all 26 | confirmed 10 | probable 16 | ≤2 km (confirmed) |
+|---|---|---|---|---|
+| published | 2.58 → 2.80 km | 1.90 → 1.87 | 4.37 → 4.45 | 7 → 7 |
+| fisheye lens | | 1.70 → 1.68 | | 8 → 6 |
+| + star azimuth | | 2.00 → 2.02 | | 6 → 5 |
+| + whole solved camera | | — → 2.02 | | — → 5 |
+
+- **These are the numbers the grid study predicted** (2026-09-25, "refined": 1.87 / 1.68 / 2.02).
+- **The within-2 km counts are the fragile ones.** Clubfire (2.07), Roundfire (2.02) and
+  Creelman (2.09) sit within 0.1 km of the line under the calibrated model.
+- **Fisheye loses Clubfire** (1.76 → 2.07). ResortFire.2 (1.87 → 0.70) and Roundfire
+  (3.39 → 2.05) are the lens's gains.
+- **Kitchen:** 0.08 → 0.14 km (published), and 0.33 under the calibrated model.
+- **Other fires:** Willow 0.30 → 0.21. ScissorsFire under the calibrated model is 0.07
+  (0.05 with star azimuth alone).
+- **PORTOLA:** WFIGS 24.03 → 23.86 km. CAL FIRE's point is now 1.24 km from the estimate
+  (was 1.36), still inside the 95% region (Δll 2.38 of 3.0).
+- **LIBERTY:** 62.10 → 63.21 km, region 154 km².
+- **The CAL FIRE agreement figures do not depend on the estimate** and are unchanged: 110
+  fires, 0.65 km median, 72 within 1 km.
+- **The Kitchen animation was re-rendered.** Three sites cross 1.22 km out at +240 s. The
+  lp-e bird enters at +600 s (37.5° off the official point) and holds the estimate at
+  1.6–1.7 km until +1380 s. Four sites then sit near 1.3 km, and close to 0.39 at +2220 s
+  and 0.14 at +2340 s. The README caption had said +660 s and 39°; the bird is in the
+  +600 s frame. `docs/likelihood-over-time.md` quotes these numbers and is updated.
+- **Bearing misses are unchanged:** outer half of the frame 4.4 → 3.5°, central half
+  2.6 → 2.5°.
+
+**All of FIgLib, terrain range** (calibrated profile, so now with the whole solved camera):
+- **Validate:** 74 bearings within 5° (was 73). 61 contain the truth at 100 px (median length
+  0.91×, unchanged) and 57 at 30 px. The terrain row at the true distance is a median 0 px
+  from the box bottom (was +1). The extra bearing and the 1 px are both POSE_FULL's pitch
+  and roll; refinement does not touch validate.
+- **Two-site confirmed median (17 fires):** 2.02 km before and after the term at cap, 100
+  and 60 px (was 1.82). At 30 px it is 2.02 → 2.07, and within 2 km goes 8 → 7.
+- **Rainbow, bearings alone: 1.42 → 2.17 km.** That is refinement, not the camera model:
+  refine=0 gives 1.42 under both FIGLIB_POSE_FULL settings, and refine=0.01 gives 2.21
+  without it and 2.17 with it. The two bearings are within 1° of collinear, so the
+  likelihood peak is a long flat ridge, and the 0.4 km grid's best cell happened to sit
+  0.75 km nearer the truth than the ridge's top. With terrain at 30 px: 2.11 km, and the region
+  25.6 → 5.0 km² (7.5 at 60, 20.6 at 100; unchanged). The README had credited the precessed
+  re-solve with 5.82 → 1.42; part of that was the grid.
+- **Palisades: 1.82 → 1.08 km, also refinement,** and the term changes nothing at any band.
+  The "peak next to dwpgm-s's tower" reading from 2026-09-25 was about the grid cell, and is
+  dropped from the README.
+- **Grove:** 8.08 → 8.31 km. tp-w's interval is unchanged (14.7–28.9 km against 31.9).
+- **Single-site figures:** confirmed miss 0.79 km / 4.42° and containment 17 of 19 ledger
+  and 13 of 18 nearest are all unchanged, because they do not solve.
+
+**Recent corpus (extra CDN cameras), center bearing, km, all sites** (grid values from
+2026-09-25 in brackets where they moved by more than 0.1 km):
+
+| fire | published | fisheye | + star az | + whole camera | sites |
+|---|---|---|---|---|---|
+| Bernardo | 1.06 (1.19) | 0.94 (1.19) | 0.90 (0.63) | 1.00 (1.19) | 8 |
+| Junction | 2.90 | 2.92 | 3.42 (3.54) | 3.42 (3.54) | 8 |
+| Thorn | 2.68 (3.37) | 2.52 (2.99) | 1.08 | 1.12 | 4 |
+| Creelman | 0.24 | 0.52 (0.65) | 1.22 | 1.21 | 9 |
+| Rainbow | 1.20 | 1.14 | 1.47 (1.60) | 1.47 (1.60) | 8 |
+| Beaver | 1.11 | 1.39 | 0.87 (0.77) | 0.90 (0.77) | 6 |
+| Sorrento | 1.10 (1.22) | 0.97 | **0.70** (1.68) | **0.70** (1.68) | 2 |
+| Church2 | 3.18 | 2.53 (2.15) | 1.50 | 1.51 | 4 |
+| Chia | 1.30 (1.47) | 1.16 | 3.66 | 3.67 | 7 |
+| Brengle | 0.76 | 0.83 | 1.07 (1.17) | 1.06 (1.17) | 10 |
+
+- **Bernardo's "whole camera costs 0.56 km" was mostly the grid.** Refined, the star-azimuth
+  and whole-camera estimates are 0.90 and 1.00.
+- **Sorrento** is a two-site fire whose refined peak sits a kilometre from its grid cell.
+- **The conclusion from 2026-09-25 stands.** Calibration fixes every large per-bearing error,
+  and loses at the fire level on Junction, Creelman, Rainbow, Chia and Brengle through
+  detection selection.
+
+**Coverage and the bias term** (`coverage`, `bias`, calibrated profile) are unchanged in
+substance:
+- **Best box:** cov95 27–41% across the six times; all boxes fall to 4% by 30 min.
+- **With the pre-hoc flag, best box:** 48% flagged; of the rest, 75% covered (LOFO 74%),
+  1.28 km, 25.7 km², 59% within 2 km. It was 47% / 74% (68%) / 1.28 / 25.7 / 58%.
+- **With the pre-hoc flag, all boxes:** 70% flagged; 74% (74%), 1.03 km, 8.1 km², 74%. It
+  was 71% / 74% (72%) / 1.12 / 9.3 / 74%.
+
+**Not moved by this pass:** the quantization and edge figures (from `accumulate.posterior`,
+not refined), the false-alarm sweep (no solve), and the skyline check (no solve).
+
+```sh
+./rerun_results.sh                               # every stage above, from a committed tree
+python -m src.figlib.settings                    # what each FIGLIB_* resolves to, and from where
+FIGLIB_PROFILE=calibrated python -m src.figlib.geolocate
+```
+
 ## Deliberately deferred
 
 Monochrome/NIR sequences (11 of them, paired with color views of the same fires) --
diff --git a/README.md b/README.md
index 2eb2c8a..7db3b84 100644
--- a/README.md
+++ b/README.md
@@ -10,10 +10,10 @@
 from t = −120 s to +2400 s and re-solved from each camera's best detection so far. Each camera's
 most confident smoke detection (right) casts a bearing from its tower; the bearings accumulate
 into a likelihood field whose peak (✗) is the estimate; the open circle is the official ignition
-coordinate. One ray has no depth; by +300 s three sites cross 1.33 km out. From +660 s
-lp-e-mobo-c's best box is a bird (apparently with very smoke-like feet), and its ray, 39° off,
-drags the estimate to 1.66 km until a stronger box on the plume replaces it at +1380 s. Four
-sites then close to **0.08 km**.*
+coordinate. One ray has no depth; by +240 s three sites cross 1.22 km out. From +600 s
+lp-e-mobo-c's best box is a bird (apparently with very smoke-like feet), and its ray, 38° off,
+drags the estimate to 1.6–1.7 km until a stronger box on the plume replaces it at +1380 s. Four
+sites then hold near 1.3 km until the last minutes' boxes close them to **0.14 km**.*
 
 Wildfire smoke detection is usually reported as mAP on a held-out split. That number does
 not tell an operator anything they can act on. This project measures three things that it
@@ -30,17 +30,18 @@ interagency [WFIGS/IRWIN](https://data-nifc.opendata.arcgis.com/) incident feed.
 
 ## Results
 
-### Geolocation: 1.90 km upper median where the ground truth is solid
+### Geolocation: 1.87 km upper median where the ground truth is solid
 
 Bearings from two or more sites, accumulated into a likelihood field over the ground, scored
-in kilometers against the official ignition coordinate. Medians in these tables are upper
+in kilometers against the official ignition coordinate. The estimate is the likelihood peak,
+re-found at 10 m around the best cell of a 0.4 km grid. Medians in these tables are upper
 medians: with an even count, the higher of the two middle values.
 
 | set | n | upper median error | max | ≤1 km | ≤2 km | ≤5 km |
 |---|---|---|---|---|---|---|
-| all scoring fires | 26 | 2.58 km | 62.10 km | 6 | 12 | 19 |
-| **name-confirmed truth** | 10 | **1.90 km** | **3.61 km** | | | |
-| probable truth | 16 | 4.37 km | 62.10 km | | | |
+| all scoring fires | 26 | 2.80 km | 63.21 km | 6 | 12 | 19 |
+| **name-confirmed truth** | 10 | **1.87 km** | **3.74 km** | | | |
+| probable truth | 16 | 4.45 km | 63.21 km | | | |
 
 ![Every scoring fire's estimate, re-centred on its official ignition point](docs/figures/geolocation_offsets.png)
 
@@ -57,13 +58,13 @@ contour, and where the open circle is the official coordinate. The inset appears
 credible region is too small to see at the main scale.*
 
 The geometry holds up in conditions that are not benign. `20201202_WillowFire` is a night
-ignition seen against continuous city light from three sites, and lands **0.30 km** from the
+ignition seen against continuous city light from three sites, and lands **0.21 km** from the
 assigned coordinate (probable-tier truth).
 
 ![Willow fire triangulation, at night against urban light](docs/figures/triangulate_willow_night.jpg)
 
 **The split between the tiers is the finding.** Every confirmed-tier fire lands within
-3.61 km; the probable tier carries the entire tail. Where a probable-tier fire fails, the
+3.74 km; the probable tier carries the entire tail. Where a probable-tier fire fails, the
 failure has a shape worth reading: bearings agreeing with each other to within a few km²
 while sitting 20–60 km from the assigned incident. That shape has two possible causes — a
 wrong record, or a weak crossing angle — and a second official source tells them apart.
@@ -71,9 +72,9 @@ wrong record, or a weak crossing angle — and a second official source tells th
 ![PORTOLA: three bearings agree tightly 24 km from the assigned incident](docs/figures/triangulate_portola.jpg)
 
 *`20171010_FIRE` → PORTOLA: three bearings from two sites close on an 8 km² region, with the
-WFIGS coordinate 24.03 km away and no ray passing near it. CAL FIRE records the same fire at
+WFIGS coordinate 23.86 km away and no ray passing near it. CAL FIRE records the same fire at
 33.50488, −117.02132 — Riverside County, De Portola Road east of Pauba Road, 23 acres —
-**1.36 km from the estimate**, inside the 95% region. The WFIGS point falls outside it
+**1.24 km from the estimate**, inside the 95% region. The WFIGS point falls outside it
 entirely, at zero relative posterior density; it reads 33°18′00″ 116°59′59″, rounded to the
 arcminute, and is filed in the wrong county.*
 
@@ -85,8 +86,8 @@ records are usually right, and PORTOLA is an outlier rather than a tier-wide fai
 also the only one of the four disagreements over 5 km that has a solve to arbitrate it.
 
 The inverse case is the guard against reading too much into a large error. `20171207_FIRE.2`
-→ LIBERTY has both sources agreeing to within 1.71 km while the estimate sits 62 km out, and
-there the geometry really is what is wrong: two cameras 13° apart, a 155 km² credible region
+→ LIBERTY has both sources agreeing to within 1.71 km while the estimate sits 63 km out, and
+there the geometry really is what is wrong: two cameras 13° apart, a 154 km² credible region
 smeared along the line of sight. A large error indicts the record only when the credible
 region is small.
 
@@ -122,15 +123,24 @@ Priced in kilometers, with the same detections and solver, on the name-confirmed
 
 | camera model | upper median error | ≤2 km |
 |---|---|---|
-| published azimuth, rectilinear lens | 1.90 km | 7 of 10 |
-| star-measured fisheye lens | **1.70 km** | **8 of 10** |
-| + per-camera star azimuth from the ledger | 2.00 km | 6 of 10 |
+| published azimuth, rectilinear lens | 1.87 km | 7 of 10 |
+| star-measured fisheye lens | **1.68 km** | 6 of 10 |
+| + per-camera star azimuth from the ledger | 2.02 km | 5 of 10 |
+| + each camera's own solved lens, pitch and roll | 2.02 km | 5 of 10 |
+
+The ≤2 km counts are the fragile number: Clubfire (2.07 km), Roundfire (2.02) and Creelman
+(2.09) all sit within 0.1 km of the line, so a tenth of a kilometre moves the count by three.
+The last row is the calibrated model (`FIGLIB_PROFILE=calibrated`). On these fires it moves no
+estimate by more than 0.03 km; it matters for the few cameras whose lens differs from the rest
+(NOTES.md, 2026-09-25).
 
 ![Bearing rays and estimates before and after calibration](docs/figures/calibration_maps.png)
 
-The lens is the clear gain: detections in the outer half of the frame go from a median bearing
-miss of 4.4° to 3.5° ([every bearing](docs/figures/calibration_bearings.png)). The azimuth
-ledger lands where detections are right: ScissorsFire goes from 0.58 km to **0.02 km**. It
+The lens is the clear gain in bearing: detections in the outer half of the frame go from a
+median miss of 4.4° to 3.5° ([every bearing](docs/figures/calibration_bearings.png)), and the
+median falls to 1.68 km. ResortFire.2 (1.87 → 0.70 km) and Roundfire (3.39 → 2.05) gain most;
+Clubfire (1.76 → 2.07) crosses the 2 km line the other way. The azimuth ledger lands where detections are right:
+ScissorsFire goes from 0.62 km to **0.07 km**. It
 also makes JunctionFire worse, and that is informative. Correcting vo-n-mobo-c by 11.3° puts
 the ignition point outside that camera's field of view, and its low-confidence detection turns
 out to be a cumulus cloud at the frame edge. With the cameras calibrated, **detection
@@ -158,9 +168,9 @@ can't be below the line, because smoke behind a crest can't be seen. It can't be
 it either, because a young plume's foot is on the terrain it rises from. Each camera with a
 star pose therefore gets one interval of distance along its bearing.
 
-- **The interval contains the fire.** On 73 bearings within 5° of the official point, it
-  contains the official distance on 60, with a median length of 0.91× that distance. At the
-  official distance, the terrain row sits a median 1 px from the box bottom.
+- **The interval contains the fire.** On 74 bearings within 5° of the official point, it
+  contains the official distance on 61, with a median length of 0.91× that distance. At the
+  official distance, the terrain row sits a median 0 px from the box bottom.
 - **One camera still says something.** Each of the 118 confirmed and probable fires seen from
   a single site gets its own figure: bearing, miss across the ray (a median of 0.79 km on
   confirmed fires), and the interval. The interval contains the official point on 30 of 37
@@ -169,22 +179,23 @@ star pose therefore gets one interval of distance along its bearing.
 The interval rarely moves a two-site estimate, but it can say how far along a shared line
 the fire is. `20260722_RainbowFire` is seen by two cameras looking straight at each other, so
 their bearings nearly coincide and give almost no distance:
-- **Bearings alone:** 1.42 km, with a 95% region 25.6 km² long along the shared line. It
-  was 5.82 km before the ledger was re-solved with precession (and bearings made exact on
-  WGS84). The two bearings are within 1° of collinear, so a fifth of a degree slides the
-  crossing kilometres. Before the fresh star solves of 2026-09-12 to -14 it was 17 km, because
-  Boucher Hill West's published azimuth was 1.16° off.
-- **With terrain, 30 px band:** still 1.42 km, but the region shrinks to 5.0 km² (7.5 km² at
+- **Bearings alone:** 2.17 km, with a 95% region 25.6 km² long along the shared line. The two
+  bearings are within 1° of collinear, so the peak is a long flat ridge: the 0.4 km grid's best
+  cell had put it at 1.42 km, and a fifth of a degree slides it kilometres. It was 5.82 km
+  before the ledger was re-solved with precession (and bearings made exact on WGS84), and 17 km
+  before the fresh star solves of 2026-09-12 to -14, because Boucher Hill West's published
+  azimuth was 1.16° off.
+- **With terrain, 30 px band:** 2.11 km, and the region shrinks to 5.0 km² (7.5 km² at
   60 px, 20.6 at 100).
 
 ![Rainbow: two opposed cameras, bearings only vs bearings with terrain ranges](docs/figures/terrain_range_rainbow.jpg)
 
 **Why it is an opt-in term, not the default.** On the 17 name-confirmed two-site fires across
-all of FIgLib, the median doesn't move at 100 px (1.82 km), and a 30 px band makes it worse
-(2.00 km) while dropping 4 of the 73 validated truths. On the poses from before precession was
-modelled, the 100 px term moved `20250107_PalisadesFire` from 1.82 to 0.32 km. On the
-precessed ledger the bearings cross next to dwpgm-s-mobo-c's own tower, and a log(0.05) penalty
-no longer outweighs the other bearing's miss, so it stays at 1.82 km. The interval is also only
+all of FIgLib, the median doesn't move at 100 px (2.02 km), and a 30 px band makes it worse
+(2.07 km) while dropping 4 of the 74 validated truths. On the poses from before precession was
+modelled, the 100 px term moved `20250107_PalisadesFire` from 1.82 to 0.32 km. With the
+precessed ledger and the refined peak, its bearings alone put it at 1.08 km, and the term
+changes nothing at any band. The interval is also only
 as good as the pose under it. Toro Peak West was re-aimed between star solves: matching
 skylines puts it 2.4° from today's pose at `20240724_GroveFire`, and the interval drawn with
 the wrong pose excludes the fire (14.7–28.9 km against 31.9 km).
@@ -280,7 +291,7 @@ Star calibration, optionally:
 ```sh
 python -m src.figlib.stars.nights 20260911 @cams.json   # moonless-night frames from HPWREN's public CDN
 python -m src.figlib.stars.run_nights                   # track, star-solve, rebuild data/meta/pose_ledger.json
-FIGLIB_LENS=fisheye FIGLIB_POSE_LEDGER=1 python -m src.figlib.geolocate
+FIGLIB_PROFILE=calibrated python -m src.figlib.geolocate  # star lens and whole solved camera
 python -m src.figlib.compare_geolocation                # baseline vs calibrated, fire by fire
 ```
 
@@ -295,6 +306,9 @@ FIGLIB_CORPUS=all python -m src.figlib.fig_bearing               # one figure pe
 FIGLIB_CORPUS=all python -m src.figlib.terrain_range figure 20260722_RainbowFire 30
 ```
 
+Every number in this README comes from one pass of `./rerun_results.sh`, which runs each
+stage above in order from a committed tree and logs it (see *Map of the code*).
+
 The Core ML work is macOS-only and installs separately (`requirements-edge.txt`); see
 [docs/edge-m3.md](docs/edge-m3.md).
 
@@ -307,7 +321,7 @@ likelihood field, and evidence accumulation end to end:
 
 ```sh
 pip install -r requirements-dev.txt
-pytest                     # ~3 s; geolocates 20240701_Kitchenfire to 0.08 km
+pytest                     # ~6 s; geolocates 20240701_Kitchenfire to 0.33 km (calibrated)
 ```
 
 CI runs this on every push. `tests/test_geom.py` pins the bearing and projection maths
@@ -331,15 +345,19 @@ Modules live in [src/figlib/](src/figlib/), with star calibration in [src/figlib
 | **Edge** | `bench_edge` `power` |
 | **Figures** | `viz` `viz_map` `viz_terrain` `animate` `animate_triangulate` `fig_peaks` `fig_pose` `fig_triangulate` `fig_bearing` `fig_offsets` · `stars.fig_solve_process` `stars.fig_moon` |
 
-Two environment variables let a whole pipeline be re-scored against different inputs without
-editing anything: `FIGLIB_DETS` points at an alternative detection directory (this is how
-quantized variants are priced in kilometers) and `FIGLIB_CAMS` at an alternative camera table.
-`FIGLIB_LENS=fisheye` and `FIGLIB_POSE_LEDGER=1` switch on the star-measured lens and the per-camera
-azimuth corrections; `geolocate` writes those variants beside the baseline, never over it.
-Two more select the data: `FIGLIB_CORPUS` scores separately fetched FIgLib archives in their
-own directories, and `FIGLIB_TIER` restricts scoring to sequences the detector cannot have
-trained on. Archive hashes and a per-run log live in `data/meta/` — see
-[data/README.md](data/README.md#corpora-and-provenance).
+Environment variables let a whole pipeline be re-scored against different inputs without
+editing anything. All of them, with their defaults, are in
+[settings.py](src/figlib/settings.py) (`python -m src.figlib.settings` lists them as resolved).
+`FIGLIB_DETS` points at an alternative detection directory (this is how quantized variants are
+priced in kilometers) and `FIGLIB_CAMS` at an alternative camera table. `FIGLIB_LENS=fisheye`,
+`FIGLIB_POSE_LEDGER=1` and `FIGLIB_POSE_FULL=1` switch on the star-measured lens, the
+per-camera star azimuths, and each camera's whole solved camera; `geolocate` writes those
+variants beside the baseline, never over it. A named profile in [configs/](configs/) sets
+several at once (`FIGLIB_PROFILE=calibrated`), and the environment overrides it. Two more
+select the data: `FIGLIB_CORPUS` scores separately fetched FIgLib archives in their own
+directories, and `FIGLIB_TIER` restricts scoring to sequences the detector cannot have trained
+on. Archive hashes and a per-run log, which records every setting as resolved, live in
+`data/meta/` — see [data/README.md](data/README.md#corpora-and-provenance).
 
 **[NOTES.md](NOTES.md) is the lab notebook** — running state, findings, and the reasoning
 behind each decision, including the predictions that were refuted and the claims that had to be
diff --git a/data/README.md b/data/README.md
index 53e2dde..17f0296 100644
--- a/data/README.md
+++ b/data/README.md
@@ -88,7 +88,8 @@ by epoch alone, with no plume-appearance offset and so no clock to score against
 
 **`meta/runs.jsonl`, `meta/<corpus>/runs.jsonl`** — one line per pipeline run: git commit and a
 hash of any uncommitted diff, package versions, the model pin, the manifest hashes it read,
-parameters, and the SHA-256 of every file it wrote. A result whose hash is not in the log did
+parameters, every FIGLIB_* setting as resolved (with its source, and the profile's hash; see
+`src/figlib/settings.py`), and the SHA-256 of every file it wrote. A result whose hash is not in the log did
 not come from a recorded run.
 
 ## Attribution
diff --git a/data/meta/all/calfire.json b/data/meta/all/calfire.json
index 1eaab9f..1d16fd3 100644
--- a/data/meta/all/calfire.json
+++ b/data/meta/all/calfire.json
@@ -8696,9 +8696,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 12.68,
-   "area95_km2": 79.5,
-   "err_calfire_km": 11.48,
+   "err_wfigs_km": 12.92,
+   "area95_km2": 78.1,
+   "err_calfire_km": 11.72,
    "sources_apart_km": 2.51,
    "improvement_km": 1.2
   },
@@ -8713,11 +8713,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 4.37,
-   "area95_km2": 7.4,
-   "err_calfire_km": 5.32,
+   "err_wfigs_km": 4.45,
+   "area95_km2": 8.0,
+   "err_calfire_km": 5.39,
    "sources_apart_km": 0.99,
-   "improvement_km": -0.95
+   "improvement_km": -0.94
   },
   {
    "fire_id": "20160711_FIRE",
@@ -8798,9 +8798,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 9.12,
+   "err_wfigs_km": 9.25,
    "area95_km2": 46.2,
-   "err_calfire_km": 8.9,
+   "err_calfire_km": 9.03,
    "sources_apart_km": 0.28,
    "improvement_km": 0.22
   },
@@ -9053,11 +9053,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 6.0,
+   "err_wfigs_km": 5.95,
    "area95_km2": 6.1,
-   "err_calfire_km": 3.51,
+   "err_calfire_km": 3.49,
    "sources_apart_km": 2.74,
-   "improvement_km": 2.49
+   "improvement_km": 2.46
   },
   {
    "fire_id": "20170713_FIRE",
@@ -9189,11 +9189,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 23.63,
-   "area95_km2": 8.5,
-   "err_calfire_km": 1.02,
+   "err_wfigs_km": 23.86,
+   "area95_km2": 8.3,
+   "err_calfire_km": 1.24,
    "sources_apart_km": 22.87,
-   "improvement_km": 22.61
+   "improvement_km": 22.62
   },
   {
    "fire_id": "20171016_FIRE",
@@ -9257,9 +9257,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 17.53,
+   "err_wfigs_km": 17.49,
    "area95_km2": 1.8,
-   "err_calfire_km": 19.5,
+   "err_calfire_km": 19.46,
    "sources_apart_km": 2.26,
    "improvement_km": -1.97
   },
@@ -9291,11 +9291,11 @@
    "n_calfire_candidates": 2,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 62.1,
-   "area95_km2": 155.4,
-   "err_calfire_km": 60.4,
+   "err_wfigs_km": 63.21,
+   "area95_km2": 154.1,
+   "err_calfire_km": 61.52,
    "sources_apart_km": 1.71,
-   "improvement_km": 1.7
+   "improvement_km": 1.69
   },
   {
    "fire_id": "20171207_Lilac",
@@ -9325,11 +9325,11 @@
    "n_calfire_candidates": 3,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 1.71,
+   "err_wfigs_km": 1.68,
    "area95_km2": 0.3,
-   "err_calfire_km": 5.11,
+   "err_calfire_km": 5.05,
    "sources_apart_km": 4.05,
-   "improvement_km": -3.4
+   "improvement_km": -3.37
   },
   {
    "fire_id": "20180517_FIRE",
@@ -9393,9 +9393,9 @@
    "n_calfire_candidates": 2,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 3.68,
+   "err_wfigs_km": 3.98,
    "area95_km2": 1.0,
-   "err_calfire_km": 3.66,
+   "err_calfire_km": 3.96,
    "sources_apart_km": 0.02,
    "improvement_km": 0.02
   },
@@ -9903,11 +9903,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 7.98,
+   "err_wfigs_km": 7.89,
    "area95_km2": 6.1,
-   "err_calfire_km": 7.05,
+   "err_calfire_km": 6.98,
    "sources_apart_km": 1.29,
-   "improvement_km": 0.93
+   "improvement_km": 0.91
   },
   {
    "fire_id": "20180813_FIRE",
@@ -10889,8 +10889,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 8.1,
-   "area95_km2": 4.0,
+   "err_wfigs_km": 7.97,
+   "area95_km2": 3.8,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -10906,7 +10906,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 27.94,
+   "err_wfigs_km": 27.75,
    "area95_km2": 2.7,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -11246,8 +11246,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 1.07,
-   "area95_km2": 4.2,
+   "err_wfigs_km": 1.34,
+   "area95_km2": 4.3,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -11297,7 +11297,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 0.42,
+   "err_wfigs_km": 0.14,
    "area95_km2": 1.1,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -11331,7 +11331,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 2.58,
+   "err_wfigs_km": 2.8,
    "area95_km2": 0.8,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -11400,10 +11400,10 @@
    "status": "matched",
    "corresponds": true,
    "err_wfigs_km": 1.9,
-   "area95_km2": 28.3,
-   "err_calfire_km": 1.54,
+   "area95_km2": 28.8,
+   "err_calfire_km": 1.55,
    "sources_apart_km": 0.39,
-   "improvement_km": 0.36
+   "improvement_km": 0.35
   },
   {
    "fire_id": "20200813_SkylineFire",
@@ -11501,8 +11501,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 1.4,
-   "area95_km2": 2.4,
+   "err_wfigs_km": 1.51,
+   "area95_km2": 2.6,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -11518,8 +11518,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 1.96,
-   "area95_km2": 11.0,
+   "err_wfigs_km": 1.97,
+   "area95_km2": 11.2,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -11535,9 +11535,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 3.61,
-   "area95_km2": 2.4,
-   "err_calfire_km": 3.85,
+   "err_wfigs_km": 3.74,
+   "area95_km2": 2.6,
+   "err_calfire_km": 3.98,
    "sources_apart_km": 0.35,
    "improvement_km": -0.24
   },
@@ -11552,8 +11552,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 0.49,
-   "area95_km2": 3.5,
+   "err_wfigs_km": 0.36,
+   "area95_km2": 3.4,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -11654,8 +11654,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 3.59,
-   "area95_km2": 3.7,
+   "err_wfigs_km": 3.39,
+   "area95_km2": 3.8,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -11705,8 +11705,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 0.3,
-   "area95_km2": 2.1,
+   "err_wfigs_km": 0.21,
+   "area95_km2": 2.2,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -11858,8 +11858,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 5.21,
-   "area95_km2": 35.8,
+   "err_wfigs_km": 5.16,
+   "area95_km2": 36.3,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -12062,11 +12062,11 @@
    "n_calfire_candidates": 2,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 0.08,
+   "err_wfigs_km": 0.14,
    "area95_km2": 3.0,
-   "err_calfire_km": 0.57,
+   "err_calfire_km": 0.43,
    "sources_apart_km": 0.51,
-   "improvement_km": -0.49
+   "improvement_km": -0.29
   },
   {
    "fire_id": "20240701_McCainfire",
@@ -12181,11 +12181,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 6.09,
-   "area95_km2": 47.8,
-   "err_calfire_km": 6.08,
+   "err_wfigs_km": 6.12,
+   "area95_km2": 47.5,
+   "err_calfire_km": 6.12,
    "sources_apart_km": 0.0,
-   "improvement_km": 0.01
+   "improvement_km": 0.0
   },
   {
    "fire_id": "20240727_Fire",
@@ -12589,9 +12589,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 3.02,
-   "area95_km2": 4.5,
-   "err_calfire_km": 2.8,
+   "err_wfigs_km": 2.91,
+   "area95_km2": 4.3,
+   "err_calfire_km": 2.69,
    "sources_apart_km": 0.23,
    "improvement_km": 0.22
   },
@@ -12725,9 +12725,9 @@
    "n_calfire_candidates": 2,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 1.7,
-   "area95_km2": 5.3,
-   "err_calfire_km": 1.7,
+   "err_wfigs_km": 1.87,
+   "area95_km2": 5.4,
+   "err_calfire_km": 1.87,
    "sources_apart_km": 0.0,
    "improvement_km": 0.0
   },
@@ -12861,11 +12861,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 1.03,
+   "err_wfigs_km": 1.12,
    "area95_km2": 0.5,
-   "err_calfire_km": 0.86,
+   "err_calfire_km": 0.97,
    "sources_apart_km": 0.66,
-   "improvement_km": 0.17
+   "improvement_km": 0.15
   },
   {
    "fire_id": "20250120_TedWilliamsFire",
@@ -13184,11 +13184,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 0.75,
+   "err_wfigs_km": 0.59,
    "area95_km2": 1.3,
-   "err_calfire_km": 0.51,
+   "err_calfire_km": 0.44,
    "sources_apart_km": 0.81,
-   "improvement_km": 0.24
+   "improvement_km": 0.15
   },
   {
    "fire_id": "20250710_SpringsFire",
@@ -13235,11 +13235,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 2.06,
+   "err_wfigs_km": 2.11,
    "area95_km2": 5.0,
-   "err_calfire_km": 2.06,
+   "err_calfire_km": 2.12,
    "sources_apart_km": 0.0,
-   "improvement_km": 0.0
+   "improvement_km": -0.01
   },
   {
    "fire_id": "20250801_BernardoFire",
@@ -13286,9 +13286,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": false,
-   "err_wfigs_km": 2.0,
-   "area95_km2": 0.2,
-   "err_calfire_km": 53.51,
+   "err_wfigs_km": 1.76,
+   "area95_km2": 0.3,
+   "err_calfire_km": 53.67,
    "sources_apart_km": 55.1,
    "improvement_km": null
   },
@@ -13405,11 +13405,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 0.82,
-   "area95_km2": 3.4,
-   "err_calfire_km": 1.27,
+   "err_wfigs_km": 0.79,
+   "area95_km2": 3.5,
+   "err_calfire_km": 1.21,
    "sources_apart_km": 0.78,
-   "improvement_km": -0.45
+   "improvement_km": -0.42
   },
   {
    "fire_id": "20260128_Fire",
@@ -13439,8 +13439,8 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 52.39,
-   "area95_km2": 134.4,
+   "err_wfigs_km": 53.19,
+   "area95_km2": 135.2,
    "err_calfire_km": null,
    "sources_apart_km": null,
    "improvement_km": null
@@ -13558,9 +13558,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 2.21,
+   "err_wfigs_km": 2.07,
    "area95_km2": 1.1,
-   "err_calfire_km": 2.21,
+   "err_calfire_km": 2.07,
    "sources_apart_km": 0.0,
    "improvement_km": 0.0
   },
@@ -13592,9 +13592,9 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": false,
-   "err_wfigs_km": 1.77,
+   "err_wfigs_km": 1.69,
    "area95_km2": 7.2,
-   "err_calfire_km": 46.14,
+   "err_calfire_km": 46.18,
    "sources_apart_km": 47.16,
    "improvement_km": null
   },
@@ -13609,11 +13609,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 2.98,
+   "err_wfigs_km": 3.81,
    "area95_km2": 25.8,
-   "err_calfire_km": 3.07,
+   "err_calfire_km": 3.89,
    "sources_apart_km": 0.18,
-   "improvement_km": -0.09
+   "improvement_km": -0.08
   },
   {
    "fire_id": "20260807_BeaverFire",
diff --git a/data/meta/all/runs.jsonl b/data/meta/all/runs.jsonl
index a1b200f..75ef05c 100644
--- a/data/meta/all/runs.jsonl
+++ b/data/meta/all/runs.jsonl
@@ -63,3 +63,15 @@
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"FIGLIB_POSE_LEDGER": "profile", "FIGLIB_POSE_LEDGER_PATH": "default", "FIGLIB_REFINE_KM": "default", "FIGLIB_TIER": "default", "FIGLIB_VARIANTS": "default", "FIGLIB_WIND_GATE": "default"}, "values": {"FIGLIB_ALLOW_UNPINNED_MODEL": "0", "FIGLIB_BAND_PX": "100", "FIGLIB_CAMS": null, "FIGLIB_CORPUS": "all", "FIGLIB_DETS": null, "FIGLIB_LENS": "fisheye", "FIGLIB_POSE_FULL": "1", "FIGLIB_POSE_LEDGER": "1", "FIGLIB_POSE_LEDGER_PATH": null, "FIGLIB_REFINE_KM": "0.01", "FIGLIB_TIER": null, "FIGLIB_VARIANTS": null, "FIGLIB_WIND_GATE": "1"}}, "stage": "fig_bearing", "started_utc": "2026-09-26T18:48:03Z", "tiers": null, "versions": {"cv2": "5.0.0", "matplotlib": "3.11.1", "numpy": "2.5.3", "python": "3.12.13"}}
diff --git a/data/meta/calfire.json b/data/meta/calfire.json
index 51f3f3c..484f984 100644
--- a/data/meta/calfire.json
+++ b/data/meta/calfire.json
@@ -2303,11 +2303,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 4.37,
+   "err_wfigs_km": 4.45,
    "area95_km2": 8.0,
-   "err_calfire_km": 5.32,
+   "err_calfire_km": 5.39,
    "sources_apart_km": 0.99,
-   "improvement_km": -0.95
+   "improvement_km": -0.94
   },
   {
    "fire_id": "20160718_FIRE",
@@ -2337,11 +2337,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 9.51,
+   "err_wfigs_km": 9.25,
    "area95_km2": 46.2,
-   "err_calfire_km": 9.3,
+   "err_calfire_km": 9.03,
    "sources_apart_km": 0.28,
-   "improvement_km": 0.21
+   "improvement_km": 0.22
   },
   {
    "fire_id": "20161113_FIRE.2",
@@ -2439,11 +2439,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 6.0,
+   "err_wfigs_km": 5.95,
    "area95_km2": 6.1,
-   "err_calfire_km": 3.51,
+   "err_calfire_km": 3.49,
    "sources_apart_km": 2.74,
-   "improvement_km": 2.49
+   "improvement_km": 2.46
   },
   {
    "fire_id": "20170722_FIRE",
@@ -2473,11 +2473,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 24.03,
+   "err_wfigs_km": 23.86,
    "area95_km2": 8.3,
-   "err_calfire_km": 1.36,
+   "err_calfire_km": 1.24,
    "sources_apart_km": 22.87,
-   "improvement_km": 22.67
+   "improvement_km": 22.62
   },
   {
    "fire_id": "20171207_FIRE.1",
@@ -2507,11 +2507,11 @@
    "n_calfire_candidates": 2,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 62.1,
+   "err_wfigs_km": 63.21,
    "area95_km2": 154.1,
-   "err_calfire_km": 60.4,
+   "err_calfire_km": 61.52,
    "sources_apart_km": 1.71,
-   "improvement_km": 1.7
+   "improvement_km": 1.69
   },
   {
    "fire_id": "20180504_FIRE",
@@ -2524,11 +2524,11 @@
    "n_calfire_candidates": 3,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 1.71,
+   "err_wfigs_km": 1.68,
    "area95_km2": 0.3,
-   "err_calfire_km": 5.11,
+   "err_calfire_km": 5.05,
    "sources_apart_km": 4.05,
-   "improvement_km": -3.4
+   "improvement_km": -3.37
   },
   {
    "fire_id": "20180602_FIRE",
@@ -2541,9 +2541,9 @@
    "n_calfire_candidates": 2,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 3.68,
+   "err_wfigs_km": 3.98,
    "area95_km2": 1.0,
-   "err_calfire_km": 3.66,
+   "err_calfire_km": 3.96,
    "sources_apart_km": 0.02,
    "improvement_km": 0.02
   },
@@ -2711,11 +2711,11 @@
    "n_calfire_candidates": 1,
    "status": "matched",
    "corresponds": true,
-   "err_wfigs_km": 7.98,
+   "err_wfigs_km": 7.89,
    "area95_km2": 6.1,
-   "err_calfire_km": 7.05,
+   "err_calfire_km": 6.98,
    "sources_apart_km": 1.29,
-   "improvement_km": 0.93
+   "improvement_km": 0.91
   },
   {
    "fire_id": "20180813_FIRE",
@@ -2983,7 +2983,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 8.1,
+   "err_wfigs_km": 7.97,
    "area95_km2": 3.8,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -3000,7 +3000,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 27.94,
+   "err_wfigs_km": 27.75,
    "area95_km2": 2.7,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -3136,7 +3136,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 0.42,
+   "err_wfigs_km": 0.14,
    "area95_km2": 1.1,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -3153,7 +3153,7 @@
    "n_calfire_candidates": 0,
    "status": "no-candidate",
    "corresponds": false,
-   "err_wfigs_km": 2.58,
+   "err_wfigs_km": 2.8,
    "area95_km2": 0.8,
    "err_calfire_km": null,
    "sources_apart_km": null,
@@ -3172,9 +3172,9 @@
    "corresponds": true,
    "err_wfigs_km": 1.9,
    "area95_km2": 28.8,
-   "err_calfire_km": 1.54,
+   "err_calfire_km": 1.55,
    "sources_apart_km": 0.39,
-   "improvement_km": 0.36
+   "improvement_km": 0.35
   },
   {
    "fire_id": "20200829_inside-Mexico",
@@ -3187,7 +3187,7 @@
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diff --git a/data/meta/recent/runs.jsonl b/data/meta/recent/runs.jsonl
index 0a874ed..83c38fc 100644
--- a/data/meta/recent/runs.jsonl
+++ b/data/meta/recent/runs.jsonl
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diff --git a/data/meta/runs.jsonl b/data/meta/runs.jsonl
index 93031c0..9b2e845 100644
--- a/data/meta/runs.jsonl
+++ b/data/meta/runs.jsonl
@@ -25,3 +25,13 @@
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diff --git a/docs/figures/calibration_bearings.png b/docs/figures/calibration_bearings.png
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diff --git a/docs/likelihood-over-time.md b/docs/likelihood-over-time.md
index 227da8a..95082ed 100644
--- a/docs/likelihood-over-time.md
+++ b/docs/likelihood-over-time.md
@@ -50,8 +50,8 @@ That is this project's situation. Camera azimuths are nameplate values: `az` is
 0/90/180/270 on 482 of 505 cameras, which is a per-station pointing bias.
 
 **The symptom is already visible.** In the Kitchen fire animation the 95% region holds at
-about 2.6 km² — an equivalent radius near 0.9 km — while the error sits at 1.33 km for
-most of the sequence. The official ignition point is outside the stated 95% region for
+about 2.4–3.2 km² — an equivalent radius near 0.9–1.0 km — while the error sits at 1.2–1.7 km
+for most of the sequence. The official ignition point is outside the stated 95% region for
 most of the fire's duration. The uncertainty is overconfident.
 
 Standard remedies:
@@ -119,7 +119,7 @@ appearance.
 constraints (Sünderhauf & Protzel, 2012) — are the temporal relatives of the α = 0.25
 uniform outlier term. They handle a transient false positive well: in the Kitchen fire
 animation, a bird in front of `lp-e-mobo-c` at 0.45 confidence moves the estimate from
-1.33 km to 1.66 km error, and it recovers when a better detection replaces it.
+1.22 km to 1.60 km error, and it recovers when a better detection replaces it.
 
 **Multi-target tracking** handles several fires, or a recurring false plume, at once:
 
diff --git a/tests/test_geolocation.py b/tests/test_geolocation.py
index 822dc66..5431811 100644
--- a/tests/test_geolocation.py
+++ b/tests/test_geolocation.py
@@ -1,9 +1,10 @@
 """End-to-end geolocation on one fire's real detections.
 
 This is the smoke test: if bearings, the projection, and the likelihood surface all agree,
-`20240701_Kitchenfire` lands within a few hundred meters of its official coordinate. The
-full corpus run reports 0.08 km here; the assertion leaves generous margin so detector
-noise or a grid-resolution change does not make it flaky.
+`20240701_Kitchenfire` lands within a few hundred meters of its official coordinate. Under
+the calibrated model pinned below it lands 0.33 km out (0.14 km on the published table); the
+assertion leaves generous margin so detector noise or a grid-resolution change does not make
+it flaky.
 """
 
 from __future__ import annotations