@@ -1,8 +1,9 @@
 # 50-Loop Execution Plan
 
-> **Status:** Strategic roadmap. Updated v0.5.56.
-> **Current state:** 1927 passing tests, pipeline AUROC 0.7792, calibration pipeline complete (intake + gate + engine + dry-run + policy-version + synthetic generator + recalibration report + batch-2 selector + recovery benchmark + end-to-end integration + pytest golden-path test + policy bump workflow + negative result archive),
+> **Status:** Strategic roadmap. Updated v0.5.61.
+> **Current state:** 1961 passing tests, pipeline AUROC 0.7792, calibration pipeline complete (intake + gate + engine + dry-run + policy-version + synthetic generator + recalibration report + batch-2 selector + recovery benchmark + end-to-end integration + pytest golden-path test + policy bump workflow + negative result archive),
 > > Phase 0 complete (Loops 1–8), Phase 1 complete (Loops 9–17), Phase 2 complete (Loops 18–29),
+> Phase 3 complete (Loops 30–40),
 > cluster-split/selectivity/triage now gated in CI, Wave 0.5 panel ready (24 candidates, 15 families), no wet-lab data yet.
 >
 > Each loop = one focused PR: bottleneck identified → implemented → verified → merged.
@@ -92,7 +93,7 @@ Build the recalibration engine gated by the policy. Implement active-learning ba
 
 ---
 
-## Phase 3 — Virtual Assay Scaffolding (Loops 30–39)
+## Phase 3 — Virtual Assay Scaffolding (Loops 30–40) ✅
 
 Build the multi-resolution virtual assay layer: structure proxies, membrane interaction models, uncertainty-aware surrogates. Every module must justify itself against cheap heuristics.
 
@@ -108,6 +109,7 @@ Build the multi-resolution virtual assay layer: structure proxies, membrane inte
 | 37 ✅ | No `--simulation-mode` CLI flag. Simulation modules exist but users cannot access them | Added `--simulation-mode` to `rank` CLI: `off` (default) and `info` (runs MembraneProxy + StructureProxy, adds `sim_*` keys to scores, includes in JSONL/report). Weighted blocked by gate (Loop 35). v0.5.57 | 6 tests, 1940 total. Demo pipeline with `--simulation-mode info` produces sim scores for all 10 candidates. Report includes sim columns |
 | 38 ✅ | No API contract for third-party simulation modules (e.g. Martini MD, AlphaFold) | `simulation/interfaces.py` extended with `ExternalSimulationAdapter` protocol. ARCHITECTURE.md extension points documented. v0.5.59 | 13 tests, 1966 total. Adapter wraps any callable, checks availability, handles errors, propagates metadata |
 | 39 ✅ | Virtual-assay benchmark report comparing simulation-augmented vs simulation-free ranking on the strict triage set | `docs/SIMULATION_BENCHMARK.md`: head-to-head report for simulation ablation, cheap-baseline comparison, and weighted-mode gate. v0.5.60. | Honest result: simulation did not improve ranking; weighted mode remains blocked. |
+| 40 ✅ | Uncertainty existed inside simulation results but did not reach selected-candidate evidence certificates | `rank --simulation-mode info` now carries `sim_membrane_uncertainty`, `sim_structure_uncertainty`, and `sim_max_uncertainty` into ranked JSONL rows, Markdown reports, and evidence certificates. v0.5.61. | Evidence certificates validate against schema with uncertainty fields; high uncertainty adds an explicit no-ranking-impact failure mode. |
 
 **Phase 3 exit criteria:**
 - At least 2 simulation modules exist and are benchmarked
@@ -118,22 +120,22 @@ Build the multi-resolution virtual assay layer: structure proxies, membrane inte
 
 ---
 
-## Phase 4 — Wet-Lab Readiness & External Validation (Loops 40–49)
+## Phase 4 — Wet-Lab Readiness & External Validation (Loops 41–50)
 
 Prepare for real wet-lab partners. Make the pack reviewable, the evidence traceable, and the process pre-registered.
 
 | Loop | Bottleneck | Deliverable | Verification |
 |------|-----------|-------------|-------------|
-| 40 | No assay-partner onboarding pack. A new CRO gets no README for how to run the panel | `docs/LAB_PARTNER_ONBOARDING.md`: panel manifest, synthesis instructions, assay protocol references, data return schema, positive/negative control sequences | A new CRO can read this and know exactly what to do |
-| 41 | No pre-registered pass/fail criteria for the Wave 1 wet-lab batch. ASSAY_PREREGISTRATION exists but criteria are not machine-checkable | `configs/wave1_pass_fail.yaml`: machine-readable pass/fail gates for Wave 1 (MIC ≤ 32 for ≥ N candidates, hemolysis ≤ 10% for ≥ M candidates, at least 1 control passes) | CI validates criteria against lab result schema |
-| 42 | No expert-review GitHub template. Review process is ad-hoc | `.github/ISSUE_TEMPLATE/expert_review.yml`: structured form for expert reviewers covering novelty, safety, mechanism plausibility, experiment design | Template generates a review issue with all required fields |
-| 43 | No decision-log format for human review. AGENTS.md requires human review for 6 decision types but no machine-readable log format exists | `schemas/decision_log.schema.json`: JSON Schema for human review decisions. Fields: date, reviewer, decision_type, evidence_refs, reasoning_notes, dissent_flag | Schema validates against real decision examples |
-| 44 | No candidate batch pack that a lab partner can download as a single artifact | `scripts/build_lab_batch_pack.py`: generates a zip with candidate CSV, evidence certificates, synthesis order, assay protocol refs, controls manifest, and data return template | `make lab-batch-pack` produces a single zip with all required files |
-| 45 | No chain-of-custody hashing for lab samples. Lab receives peptides but can't verify identity | Add SHA-256 hash of each candidate sequence and synthesis order to the batch pack. Lab can verify received peptides match | Hash verification script works end-to-end on synthetic data |
-| 46 | No pre-registered analysis plan for Wave 1 results. What analysis runs when data comes back? | `docs/WAVE1_ANALYSIS_PLAN.md`: pre-registered statistical analysis (primary: MIC ≤ 32 hit rate; secondary: selectivity index; exploratory: per-family correlation). No post-hoc changes allowed | Plan is timestamped and locked before data return |
-| 47 | No data-return validation tool. Lab sends CSV/JSON files; pipeline needs to validate them against schema | `scripts/validate_lab_data_return.py`: checks returned data against `schemas/lab_result.schema.json`, reports missing fields, out-of-range values, control failures | Returns clear error messages for each validation failure mode |
-| 48 | No public-data release package. If results are publishable, what goes in the supplement? | `docs/PUBLICATION_PACK.md`: checklist for public release — benchmark data, evidence certificates, candidate scores, negative results, analysis scripts, README for reproduction | Pack is complete enough for a reviewer to reproduce all findings |
-| 49 | **Culmination loop**: run the full dry-lab pipeline end-to-end, calibrate against any available wet-lab data, produce the final report. Pre-register the discovery claim | Full reproducibility report: all outputs regenerated from versioned inputs, benchmark card finalized, calibration report generated, discovery probability estimated with honest uncertainty | `make full-reproducibility-report` succeeds; all 49 gates pass; report is publishable as a preprint |
+| 41 | No assay-partner onboarding pack. A new CRO gets no README for how to run the panel | `docs/LAB_PARTNER_ONBOARDING.md`: panel manifest, synthesis instructions, assay protocol references, data return schema, positive/negative control sequences | A new CRO can read this and know exactly what to do |
+| 42 | No pre-registered pass/fail criteria for the Wave 1 wet-lab batch. ASSAY_PREREGISTRATION exists but criteria are not machine-checkable | `configs/wave1_pass_fail.yaml`: machine-readable pass/fail gates for Wave 1 (MIC ≤ 32 for ≥ N candidates, hemolysis ≤ 10% for ≥ M candidates, at least 1 control passes) | CI validates criteria against lab result schema |
+| 43 | No expert-review GitHub template. Review process is ad-hoc | `.github/ISSUE_TEMPLATE/expert_review.yml`: structured form for expert reviewers covering novelty, safety, mechanism plausibility, experiment design | Template generates a review issue with all required fields |
+| 44 | No decision-log format for human review. AGENTS.md requires human review for 6 decision types but no machine-readable log format exists | `schemas/decision_log.schema.json`: JSON Schema for human review decisions. Fields: date, reviewer, decision_type, evidence_refs, reasoning_notes, dissent_flag | Schema validates against real decision examples |
+| 45 | No candidate batch pack that a lab partner can download as a single artifact | `scripts/build_lab_batch_pack.py`: generates a zip with candidate CSV, evidence certificates, synthesis order, assay protocol refs, controls manifest, and data return template | `make lab-batch-pack` produces a single zip with all required files |
+| 46 | No chain-of-custody hashing for lab samples. Lab receives peptides but can't verify identity | Add SHA-256 hash of each candidate sequence and synthesis order to the batch pack. Lab can verify received peptides match | Hash verification script works end-to-end on synthetic data |
+| 47 | No pre-registered analysis plan for Wave 1 results. What analysis runs when data comes back? | `docs/WAVE1_ANALYSIS_PLAN.md`: pre-registered statistical analysis (primary: MIC ≤ 32 hit rate; secondary: selectivity index; exploratory: per-family correlation). No post-hoc changes allowed | Plan is timestamped and locked before data return |
+| 48 | No data-return validation tool. Lab sends CSV/JSON files; pipeline needs to validate them against schema | `scripts/validate_lab_data_return.py`: checks returned data against `schemas/lab_result.schema.json`, reports missing fields, out-of-range values, control failures | Returns clear error messages for each validation failure mode |
+| 49 | No public-data release package. If results are publishable, what goes in the supplement? | `docs/PUBLICATION_PACK.md`: checklist for public release — benchmark data, evidence certificates, candidate scores, negative results, analysis scripts, README for reproduction | Pack is complete enough for a reviewer to reproduce all findings |
+| 50 | **Culmination loop**: run the full dry-lab pipeline end-to-end, calibrate against any available wet-lab data, produce the final report. Pre-register the discovery claim | Full reproducibility report: all outputs regenerated from versioned inputs, benchmark card finalized, calibration report generated, discovery probability estimated with honest uncertainty | `make full-reproducibility-report` succeeds; all 50 gates pass; report is publishable as a preprint |
 
 **Phase 4 exit criteria:**
 - Lab partner onboarding pack exists
@@ -157,9 +159,9 @@ Phase 1 (Loops 9–17): Benchmark Honesty
   └── justifies →
 Phase 2 (Loops 18–29): Calibration Engine
   └── requires real data ──┐
-Phase 3 (Loops 30–39): Virtual Assay       │ (parallel tracks)
+Phase 3 (Loops 30–40): Virtual Assay       │ (parallel tracks)
   └── feeds into ──────────┘
-Phase 4 (Loops 40–49): Wet-Lab Readiness ←── both tracks
+Phase 4 (Loops 41–50): Wet-Lab Readiness ←── both tracks
 ```
 
 **Parallel tracks:** Loops 30–39 (virtual assay) and Loops 18–29 (calibration engine) are
@@ -180,8 +182,8 @@ If no data arrives, virtual assay scaffolding continues independently.
 Phase 0: ✅ Complete (Loops 1–8)
 Phase 1: ✅ Complete (Loops 9–17)
 Phase 2: ✅ Complete (Loops 18–29)
-Phase 3: In progress — Loop 38 ✅ (Loops 30–39)
-Phase 4: Not started (Loops 40–49)
+Phase 3: ✅ Complete (Loops 30–40)
+Phase 4: Not started (Loops 41–50)
 ```
 
 ### Completed
@@ -241,7 +243,7 @@ Phase 4: Not started (Loops 40–49)
 | 28 ✅ | Policy version bump workflow for when real data arrives | `scripts/bump_recalibration_policy.py`: standalone script with `--dry-run`, decision-log guard, auto-increment + write. CI guard in `ci.yml` validates policy version changes against base branch. v0.5.49. 9 tests. | CI rejects policy PRs without valid decision log; `make bump-policy-version` and `make bump-policy-version-dry-run` available |
 | 29 ✅ | No public negative-result archive format. If Wave 1 yields all negatives, where do they go? | `docs/NEGATIVE_RESULT_ARCHIVE.md`: full template with entry schema, procedures, automation notes, and limitations. Covers pre-selection rejects, selected-untested, lab inactives, lab toxic, control failures. v0.5.50. | Template complete enough for a lab partner to fill; schema defines 18 fields with required/conditional markers |
 
-**Next loop:** Loop 40 — lab partner onboarding pack.
+**Next loop:** Loop 41 — lab partner onboarding pack.
 
 **Phase 2 exit criteria (all 5 met ✅):**
 - ✅ `make calibration-loop` runs from clean checkout, produces batch-2 manifest
 
@@ -5,7 +5,8 @@ Machine-readable snapshot: `outputs/metrics_snapshot.json` regenerated with `mak
 > **Purpose:** One authoritative table of current pipeline metrics. If any doc disagrees
 > with this file, this file wins. Updated whenever benchmark/benchmark config changes.
 >
-> **Last updated:** 2026-07-06 (simulation benchmark report — v0.5.60)
+> **Last updated:** 2026-07-06 (simulation uncertainty in evidence — v0.5.61)
+> **New in v0.5.61:** `rank --simulation-mode info` now propagates `sim_membrane_uncertainty`, `sim_structure_uncertainty`, and `sim_max_uncertainty` into ranked outputs and selected-candidate evidence certificates. This is reviewability only; `weighted` remains blocked. 1 focused regression file updated. 1961 total.
 > **New in v0.5.60:** `docs/SIMULATION_BENCHMARK.md` consolidates simulation ablation, cheap-baseline comparison, and weighted-mode gate results. Current conclusion: simulation does not improve ranking; `weighted` remains blocked.
 > **New in v0.5.59:** `ExternalSimulationAdapter` protocol — wraps third-party callables into `VirtualAssayProxy`. Availability check, graceful error handling, metadata override. ARCHITECTURE.md docs updated. 12 tests. 1965 total.
 > **New in v0.5.58:** Per-signal cheap-baseline comparison: 0/4 simulation signals beat their cheapest heuristic. All simulation modules remain permanently experimental. `make bench-simulation-baselines`. 13 tests. 1953 total.
@@ -45,7 +46,7 @@ Machine-readable snapshot: `outputs/metrics_snapshot.json` regenerated with `mak
 > **New in v0.5.31:** Added dipeptide-order features for sequence-order awareness. `dipeptide_order_score` achieves AUROC 0.7861 on AMP-vs-scrambled discrimination — the strongest order-dependent feature in the pipeline. Only 7/31 features survive scrambling (amphipathicity/helix-wheel + dipeptide). All composition features are purely position-independent (exactly 0.5000 AUROC on scrambled test).
 > **New in v0.5.30:** Easy baseline benchmark added — charge density alone (AUROC 0.8166) outperforms the full pipeline ensemble (0.7792) on AMP-vs-Swiss-Prot-decoy discrimination. Honest finding documented: expected because pipeline optimizes for safety, not raw discrimination.
 > **New in v0.5.29:** Expanded benchmark to 500 AMPs + 500 composition-matched decoys (n=1000). AUROC 0.7792 (CI₉₅: 0.7505–0.8065) confirms signal generalizes. Cluster-aware CI: 0.746–0.8102. Representative AUROC: 0.778. Standard benchmark (n=191) retained for backward comparison.
-> **Pipeline version:** v0.5.60
+> **Pipeline version:** v0.5.61
 > **Branch:** main
 
 ---
@@ -1115,7 +1116,7 @@ Decoys score low on activity. Selective AMPs score moderately on both.
 
 | Metric | Value |
 |--------|-------|
-| Total tests | 1965 |
+| Total tests | 1961 |
 | Coverage (branch) | 99% (6 CLI guard lines only) |
 | Source modules at 100% | All pipeline, QC, scoring modules |
 
 
@@ -1,5 +1,32 @@
 # Roadmap
 
+## v0.5.61 — Simulation Uncertainty in Evidence (Loop 40) ✓ (2026-07-06)
+
+Closed the last concrete Phase 3 exit-criterion gap: simulation uncertainty now
+travels with selected candidates instead of living only inside proxy return
+objects.
+
+Changes:
+- ``src/openamp_foundry/pipeline.py`` — `rank --simulation-mode info` now
+  writes three informational uncertainty fields into candidate scores:
+  ``sim_membrane_uncertainty``, ``sim_structure_uncertainty``, and
+  ``sim_max_uncertainty``. Candidates above the `0.5` uncertainty bar gain an
+  explicit failure-mode note stating that simulation outputs must not affect
+  selection.
+- ``schemas/candidate.schema.json`` — documents the three uncertainty keys so
+  evidence certificates can carry them without schema drift.
+- ``tests/test_simulation_mode_cli.py`` — covers ranked output, report columns,
+  evidence-certificate inclusion, and schema validation for the new fields.
+- ``docs/VIRTUAL_ASSAY_SCOPE.md`` and ``docs/SIMULATION_BENCHMARK.md`` — synced
+  to current behavior.
+- ``docs/50_LOOP_PLAN.md`` and ``docs/METRICS_CURRENT.md`` — current-position
+  drift fixed; Loop 40 recorded complete.
+
+Honest finding:
+- This does not improve ranking.
+- It improves reviewability: virtual-assay outputs now carry their own
+  uncertainty wherever selected-candidate evidence is consumed.
+
 ## v0.5.60 — Simulation Benchmark Report (Loop 39) ✓ (2026-07-06)
 
 Closed the benchmark-report gap with `docs/SIMULATION_BENCHMARK.md`.
 
@@ -70,4 +70,7 @@ Until this report changes with stronger evidence:
 - `weighted` simulation remains blocked.
 - Evidence certificates may include simulation values only as experimental
   context, not as activity, safety, or selectivity proof.
+- Evidence certificates now include `sim_membrane_uncertainty`,
+  `sim_structure_uncertainty`, and `sim_max_uncertainty` when simulation
+  info mode is used.
 - No public claim may imply simulation-validated antimicrobial activity.
@@ -18,6 +18,9 @@ have explicit ablation evidence. Current state:
   `rich_selectivity` on hemolysis detection.
 - `bench simulation-gate` therefore blocks `weighted` mode and downgrades to
   `info` until the evidence changes.
+- `rank --simulation-mode info` propagates membrane, structure, and maximum
+  simulation uncertainty into ranked JSONL rows, Markdown reports, and evidence
+  certificates. These values are informational only.
 
 ## Core Principle
 
@@ -70,6 +73,12 @@ surface that directly. A module with `uncertainty > 0.5` (on a 0–1 scale
 where 0 = fully constrained, 1 = unconstrained) is experimental and must
 not affect selection.
 
+When `rank --simulation-mode info` is used, selected-candidate evidence
+certificates include `sim_membrane_uncertainty`,
+`sim_structure_uncertainty`, and `sim_max_uncertainty`. These fields expose
+proxy uncertainty for reviewers; they do not change ranking, filtering, or
+selection.
+
 ## Ablation Requirement
 
 Each module must pass an ablation benchmark before it can affect
@@ -149,6 +158,6 @@ Phase 3 is complete when:
    `weighted` mode.
 3. Modules that fail the ablation are either removed or permanently
    marked experimental.
-4. Uncertainty is propagated through to the evidence certificate.
+4. Uncertainty is propagated through to the evidence certificate. ✅
 5. The external adapter protocol (`ExternalSimulationAdapter`) is
    documented in ARCHITECTURE.md.
@@ -38,6 +38,18 @@
         "disagreement": {
           "type": "number", "minimum": 0, "maximum": 1,
           "description": "Absolute difference between activity_likeness and boman_activity. High values indicate scoring uncertainty."
+        },
+        "sim_membrane_uncertainty": {
+          "type": "number", "minimum": 0, "maximum": 1,
+          "description": "Experimental membrane-proxy uncertainty. Informational only unless weighted simulation is explicitly allowed."
+        },
+        "sim_structure_uncertainty": {
+          "type": "number", "minimum": 0, "maximum": 1,
+          "description": "Experimental structure-proxy uncertainty. Informational only unless weighted simulation is explicitly allowed."
+        },
+        "sim_max_uncertainty": {
+          "type": "number", "minimum": 0, "maximum": 1,
+          "description": "Maximum virtual-assay proxy uncertainty for this candidate. Values above 0.5 must not affect selection."
         }
       }
     },