spec: six-layer fingerprint stack — classification that can abstain (SP-FPRINT-STACK-001)

CHANGELOG.md

@@ -11,6 +11,7 @@ The format follows [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). Thi
 ### Added
 - Image-trust contracts — the Assay's measured-not-asserted discipline applied to images, for both OS and agents: `ImageTrustReport` (per-dimension trust checks — reproducibility, attestation_verify, sbom_completeness, signature_verify, measured_boot, provenance_depth — each carrying a `Measurement` via `$ref`, plus a render-time `projectedTrust` ok/sad/bad verdict; unifies OS and agent images through `subjectKind`) and `AgentImage` (agent runtime-artifact identity + provenance parallel to `OSImage`, distinct from process-level `AgentPassport`). `ImagePromotionGate` now requires a `trustReportRef` when `decision: approved` (non-breaking if/then), and the new `validate-image-trust-examples` target enforces projection soundness plus the cross-document rule that an approval must reference a report projecting `ok` — closing, for images, the self-assertable-approval gap the Assay hardening closed for verifiers. Reuses `Measurement`'s gate-eligibility invariants for free via `$ref` (a declared/assumed check cannot reach `ok`). All invariants verified biting. See `schemas/image-trust.README.md`.
 - Assay fleet-tier contracts (cloud-mesh): `AssayRollup` (fleet/cohort aggregate of `ReasoningAssay` verdicts over a window — ok/sad/bad distribution, calibration-drift view of which `AssayStandard` versions are live across the fleet, unassayed-reason breakdown) and `AssayStandardRollout` (canary→widen/halt promotion of a new `AssayStandard` version across cohorts, gated on an observed `AssayRollup`; rides the release-bundle + lifecycle machinery). New `validate-assay-fleet-examples` target enforces aggregation/rollout soundness (distribution sums, drift-flag agreement, rolloutPct vs node share, decision/phase consistency, and no-promotion-by-hope), all verified biting. This is the boundary between prophet-mesh single-user-local (node self-view, no fleet dashboards, structural) and cloud-mesh (rollup + rollout + dashboards). See `schemas/assay-fleet.README.md`.
+- **Six-layer fingerprint stack (SP-FPRINT-STACK-001)** — classification that can distinguish "I don't know" from "my evidence contradicts itself". `ClassificationStance` over FOUR (POS/NEG/**ZERO**/**INADMISSIBLE**) with the support-vs-plausibility reading tag in the TYPE (DR-5); `FingerprintLayerEvidence` with per-layer discriminated witnesses for L1 OntoDT / L2 OntoDQ / L3 glossary / L4 operational semantics / L5 table topic / L6 key graph; `ColumnFingerprint` (pool -> guard -> quantize, in that order); `ColumnDriftObservation` (L5-D1/L5-D2, blocking); `EstateAdmissibilityReport` (the phase-0 deliverable — which layers this estate can trust, shipped BEFORE any classification); `ClassificationEnforcementPolicy` (**DR-4 settled**: POS/NEG not knobs, INADMISSIBLE never fail-open, ZERO an attested per-resource-class knob unavailable at confidential/restricted). Recompute-don't-trust throughout: stance from evidence, pooling from admissible layers, `n_eff` from the covariance spectrum (participation ratio — Herfindahl demoted to a cheap precheck, since two perfectly correlated layers give H=0.5 while supplying one layer's worth of information), drift from the measured distance. Axiom **X1** (parthood and subtyping disjoint; closure traverses subtyping only; open-world counts are intervals with INADMISSIBLE reported separately) enforced as a CI invariant. `DataClass.classifier.kind` const **retired**: TensorFlow Lattice was archived by its owner in April 2026, so the pin now enumerates the structurally-monotone constructions with the dead name deprecated-but-accepted, and gains a `fingerprintStack` binding. 81 checks + 9 schema negative vectors + **25 gates proven to bite** by mutation harness + an M5 property test verified against a deliberately non-monotone aggregator (the #265 vacuous-constraint defect, guarded against structurally this time).
 - Trained DataClass classifiers — adversarial hardening (follow-up to #264): stronger fixture where BOTH monotone features (token_magnitude, decimal_places) genuinely drive the label so the monotone constraint actually BINDS (was vacuous — decimal_places held constant); standardiser fit on the TRAIN split only (no test leakage); ModelManifest named honestly `monotone-logistic` (a realisation of the declared tf-lattice-wide-and-deep contract, not a full lattice). Validator now checks BOTH per-class heads monotone AND asserts the constraint binds (non-vacuous), and compares reproduced accuracy at the stored 4-decimal precision (fixes a latent tolerance bug the trivial 1.0-accuracy fixture had masked). 8 teeth.
 - Trained DataClass classifiers v0.1 — specified→trained→registered→reproduced (closes the program's last owed thread): `tools/train_dataclass_classifiers.py` trains real per-class LOGISTIC (one-vs-rest) + per-table SOFTMAX heads, MONOTONE-constrained in `monotonicFeatures` (weight >=0 projection = TF-Lattice essence), deterministically; emits trained weights + a conformant `ModelManifest` (id=DataClass modelRef) + train/eval `RunRecord`s (ids=runRef/evalRunRef) so the previously-dangling DataClass classifier references now RESOLVE. `validate-trained-classifiers` (recompute-don't-trust): digest matches weight bytes; eval accuracy REPRODUCED from weights+fixture (inflated claim fails); model verified genuinely monotone (perturb up → score never drops); refs resolve; artifacts schema-conform. Teeth-verified (tampered weights refused). Uses numpy.
 - Learned spell-correction + user dictionary v0.1 — from context, not dictionary matching (task #13): `tools/learned_dictionary.py` decides each UNKNOWN token from a count-based skip-gram word-sense predictor (PPMI + truncated SVD; SGNS≈PPMI-SVD) instead of a static wordlist. LEARN a token that recurs with a coherent word-sense (a real domain term the dictionary hadn't seen); CORRECT a rare token to the known word its SENSE matches (skip-gram cosine picks the target, not edit distance alone — a near-spelled but different-sense token is not auto-corrected); leave the rest UNKNOWN (fail-closed, human-admitted). Every decision is a proposal, never a silent rewrite. `validate-learned-dictionary` teeth: epistemiclevel learned, reciept->receipt by sense, qwzptl unknown, learned term never auto-corrected. Uses numpy.

Makefile

@@ -1,6 +1,6 @@
-.PHONY: validate validate-resource-contract validate-measurement validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-ingestion-pipeline-examples validate-data-acquisition-examples validate-glossary-alignment-examples validate-data-class-examples validate-table-keys validate-dag-loop validate-vocab-currency-loop vocab-currency-dogfood validate-glossary-promotion validate-agreement validate-stopword-analysis stopword-analysis-live validate-kgram-differential validate-learned-dictionary train-dataclass-classifiers validate-assay-fleet-examples validate-image-trust-examples validate-schema-references
+.PHONY: validate validate-resource-contract validate-measurement validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-ingestion-pipeline-examples validate-data-acquisition-examples validate-glossary-alignment-examples validate-data-class-examples validate-table-keys validate-dag-loop validate-vocab-currency-loop vocab-currency-dogfood validate-glossary-promotion validate-agreement validate-stopword-analysis stopword-analysis-live validate-kgram-differential validate-learned-dictionary train-dataclass-classifiers validate-assay-fleet-examples validate-image-trust-examples validate-schema-references validate-fingerprint-stack
 
-validate: validate-data-class-examples validate-glossary-alignment-examples validate-data-acquisition-examples validate-ingestion-pipeline-examples validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-resource-contract validate-table-keys validate-dag-loop validate-vocab-currency-loop validate-glossary-promotion validate-agreement validate-stopword-analysis validate-kgram-differential validate-learned-dictionary validate-trained-classifiers validate-multiverseal-twin-examples validate-assay-fleet-examples validate-image-trust-examples validate-schema-references
+validate: validate-data-class-examples validate-glossary-alignment-examples validate-data-acquisition-examples validate-ingestion-pipeline-examples validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-resource-contract validate-table-keys validate-dag-loop validate-vocab-currency-loop validate-glossary-promotion validate-agreement validate-stopword-analysis validate-kgram-differential validate-learned-dictionary validate-trained-classifiers validate-multiverseal-twin-examples validate-assay-fleet-examples validate-image-trust-examples validate-schema-references validate-fingerprint-stack
 	@echo "OK: validate"
 
 validate-assay-fleet-examples:
@@ -24,6 +24,11 @@ validate-ingestion-pipeline-examples:
 	python3 -m pip install --user jsonschema >/dev/null
 	python3 tools/validate_ingestion_pipeline_examples.py
 
+validate-fingerprint-stack:
+	python3 -m pip install --user jsonschema >/dev/null
+	python3 tools/validate_fingerprint_stack.py
+	python3 tools/test_fingerprint_stack_teeth.py
+
 validate-data-class-examples:
 	python3 -m pip install --user jsonschema >/dev/null
 	python3 tools/validate_data_class_examples.py

examples/classification_enforcement_policy.json

@@ -0,0 +1,35 @@
+{
+  "id": "urn:srcos:classification-enforcement:estate-default-v1",
+  "type": "ClassificationEnforcementPolicy",
+  "specVersion": "2.0.0",
+  "posBehavior": "allow",
+  "negBehavior": "deny",
+  "inadmissibleBehavior": "fail-closed",
+  "resourceClasses": [
+    {
+      "resourceClass": "reference-lookup",
+      "sensitivity": "public",
+      "zeroBehavior": "fail-open",
+      "attestationRef": "urn:srcos:attestation:dr4-reference-lookup-2026-08-01",
+      "rationale": "Published reference tables carry no personal data and over-blocking them strands every downstream join. Serving an unclassified reference column is a known, bounded exposure that the data steward accepted on the record."
+    },
+    {
+      "resourceClass": "operational-analytics",
+      "sensitivity": "internal",
+      "zeroBehavior": "fail-closed",
+      "attestationRef": null,
+      "rationale": "Default. An unclassified column in an internal analytics surface is more likely to be an un-onboarded field than a deliberately open one."
+    },
+    {
+      "resourceClass": "customer-master",
+      "sensitivity": "restricted",
+      "zeroBehavior": "fail-closed",
+      "attestationRef": null,
+      "rationale": "Customer master is where the personal data is. A column we cannot classify is exactly the column not to serve."
+    }
+  ],
+  "abstentionBudget": {
+    "maxZeroFraction": 0.35,
+    "onExceed": "escalate-human"
+  }
+}

examples/column_drift.repurposed_status.json

@@ -0,0 +1,23 @@
+{
+  "id": "urn:srcos:column-drift:ops-orders-status-code-2026-08",
+  "type": "ColumnDriftObservation",
+  "specVersion": "2.0.0",
+  "subjectColumnRef": "urn:srcos:column:ops.orders.status_code",
+  "baseline": {
+    "profileRunId": "profile-2026-02-02-ops-orders",
+    "schemaVersion": "ops.orders@v12",
+    "observedAt": "2026-02-02T03:15:00Z"
+  },
+  "current": {
+    "profileRunId": "profile-2026-08-03-ops-orders",
+    "schemaVersion": "ops.orders@v12",
+    "observedAt": "2026-08-03T04:40:00Z"
+  },
+  "metric": "jensen-shannon",
+  "distance": 0.61,
+  "threshold": 0.15,
+  "schemaChanged": false,
+  "drifted": true,
+  "verdict": "silent-repurpose",
+  "stewardQueueRef": "urn:srcos:steward-queue:data-governance-drift"
+}