{
  "_meta": {
    "gap": "G7",
    "form": "(b) named queue into the human review phase",
    "ratified": "2026-08-03",
    "rationale": "Measured in R11 §9.6: 2 of the 4 subtractions applied in 957ebef left a residue, so residues are the normal by-product of a delete-only envelope rather than an exception. Two of the members are replacements, not multi-locator cases, which a deletion-oriented O4 class would not have fixed. A queue absorbs both classes; an O4 return contract would have been mis-sized against the evidence.",
    "contract": "Anchors are verbatim strings, never line numbers (line ≠ real_line in 9 of 17 R11 records). An open entry whose anchor no longer occurs in its file is drift, and check-g7-queue.mjs fails rather than passing it silently. Nothing in this queue is machine-appliable by definition — every entry is outside the O2 envelope. Resolution is a human review act.",
    "evidence": "docs/r11-pilot-results.md §9.4, §9.5, §9.6; docs/ref-kb-correctness-program-2026-06.md §8 G7"
  },
  "entries": [
    {
      "id": "idx-17",
      "file": "skills/ms-ai-engineering/references/rag-architecture/rag-caching-optimization.md",
      "class": "replacement",
      "status": "resolved",
      "raised": "2026-08-03",
      "summary": "The idx 17 subtraction (957ebef) deleted the three score-threshold bands but left the lead-in ending in a colon, promising an enumeration that no longer existed, immediately followed by a **Verified** stamp. The subtraction was correct; the paragraph it left was not. V1/V2/V2b/V3 are string invariants over deleted text and cannot see document coherence, so the machine could not have caught it.",
      "evidence": "docs/r11-pilot-results.md §9.6",
      "anchors": [],
      "resolution": "Operator-ratified 2026-08-03: colon changed to a period, making the lead-in a complete and independently true sentence that the **Verified** stamp correctly covers. Corpus swept for the same defect shape (bold lead-in ending in colon, blank line, **Verified**) — no other occurrence."
    },
    {
      "id": "idx-33",
      "file": "skills/ms-ai-security/references/ai-security-engineering/ai-threat-modeling-stride.md",
      "class": "replacement",
      "status": "open",
      "raised": "2026-08-03",
      "summary": "The idx 33 subtraction removed AI asset inventory via Azure Resource Graph and the Purview Insider Risk Management bullet from an unsupported Defender for Cloud AISPM attribution. Correct — but the same capabilities survive in this file under Cloud Adoption Framework Secure AI attribution, stamped 'Verified MCP 2026-04', and are asserted in four other corpus files. The edit's benefit is corpus-wide smaller than the single line suggested. Whether the CAF attribution is itself supported has not been checked.",
      "evidence": "docs/r11-pilot-results.md §9.5 (cross-corpus check), §9.6",
      "anchors": [
        "Oppdatert 2026-04: inkluderer nå AI asset inventory via Azure Resource Graph"
      ]
    },
    {
      "id": "idx-26",
      "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
      "class": "multi-locator",
      "status": "resolved",
      "raised": "2026-08-03",
      "summary": "The Responsible AI Scorecard component list names Error analysis (item 4) and Counterfactual analysis (item 5). Both were measured false against first-party docs 2026-08-03: the canonical scorecard segments are summary/model overview, data analysis, model performance, cohorts, top important factors, fairness insights and causal insights; Error analysis and Counterfactual analysis are Responsible AI *dashboard* components. The delete-only reduction could only remove item 5, because line 300 asserts Error analysis as scorecard content too — so a partial fix would have left a known-false claim standing while introducing a renumbering artifact (1,2,3,4,6,7). Operator declined the partial fix 2026-08-03 and sent the whole case here. Correct repair spans both locators.",
      "evidence": "docs/r11-pilot-results.md §9.6; https://learn.microsoft.com/azure/machine-learning/how-to-responsible-ai-scorecard",
      "anchors": [
        "4. **Error analysis**: Error rates per cohort, confusion matrices",
        "| **Risk assessment** | Responsible AI Scorecard: Error analysis, fairness assessment |"
      ],
      "resolution": "Operator-ratified 2026-08-03, form (b) — relabel rather than remove. Both source pages were re-fetched live this session and confirm the measurement independently of §9.6: how-to-responsible-ai-scorecard enumerates summary/model overview, data analysis, model performance, cohorts, top important factors, fairness insights and causal insights; concept-responsible-ai-dashboard lists Error analysis and Counterfactual what-if among the dashboard components. Locator 1: items 4 and 5 removed from the numbered scorecard list, which renumbers cleanly to 1-5 — the 1,2,3,4,6,7 artifact was forced only inside the delete-only envelope and does not apply to an ordinary Edit. The two capabilities are retained in a blockquote explicitly marked as dashboard components rather than scorecard segments, so genuine source-confirmed information survives and the reader is warned off precisely the conflation that produced the defect. Locator 2: the Risk assessment row now reads 'fairness insights' alone. The **Confidence:** Verified stamp twelve lines below vouched for the false list and was silently outside every machine check (V1/V2/V2b/V3 are string invariants; check-g7-queue only tests anchors); it is kept but dated to 2026-08-03 to record the re-verification. Document coherence around both locators was read after the edit, per the c569bdc lesson. A neighbouring defect surfaced by the file sweep is booked separately as idx-26b rather than folded in here."
    },
    {
      "id": "idx-26c",
      "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
      "class": "replacement",
      "status": "resolved",
      "raised": "2026-08-03",
      "summary": "The scorecard enumeration is incomplete, and idx 26's repair made that assertion active rather than latent. The live fetch performed for idx 26 lists seven canonical segments: summary/model overview, data analysis, model performance, cohorts, top important factors, fairness insights, causal insights. The list after the idx 26 edit carries five — model overview, fairness assessment, model interpretability, causal inference, data quality — so **model performance** and **cohorts** are absent. Note for whoever repairs this: 'Cohort analysis' does appear a few lines below, but inside the *Customization* block, not as an enumerated segment, so it does not cure the omission. This was a latent incompleteness under an undated stamp before 2026-08-03; dating the stamp to 2026-08-03 as part of idx 26 turned it into a positive claim that this enumeration was verified that day, over content the same day's verification showed to be missing two members — the same class of defect §9.7 describes, where an anchor-correct edit leaves a false claim where no check reaches. Booked rather than folded into idx 26, whose ratified scope was the falsity of items 4 and 5, not the completeness of the list. The date on the stamp is deliberately NOT re-litigated here: the relabel it covers WAS verified 2026-08-03, and the operator may keep it once this entry closes the completeness gap. Adding the two missing segments is a content change requiring its own operator ratification.",
      "evidence": "docs/r11-pilot-results.md §9.7; https://learn.microsoft.com/azure/machine-learning/how-to-responsible-ai-scorecard",
      "anchors": [
        "5. **Data quality**: Dataset statistics, missing values, outlier analysis"
      ],
      "resolution": "Operator-ratified 2026-08-03, form (a) - add the two missing segments rather than downgrade the list to a selection. The source was re-fetched live this session and enumerates seven segments in order: summary/model overview, data analysis, model performance, cohorts, top important factors, fairness insights, causal insights. Model performance and Cohorts were appended as items 6 and 7; the existing five were left untouched, so the change is confined to the measured gap. Appending rather than inserting in source order was a free choice, not a machine-forced one: STATE asserted that a resolved entry anchor must stop matching or the check fails, and that is wrong - lib/g7-queue.mjs:69-75 returns before the anchor check for resolved entries, so resolved is fully exempt and anchor drift only bites an entry left open. The list carries no ordering claim, and the existing five were already not in source order, so appending introduces no new falsity. Item 7 is worded \"automatisk uttrukket av scorecard-en\" to keep it distinct from the Cohort analysis bullet in the Customization block, which is operator-defined and does not enumerate a segment. The **Confidence:** Verified (MCP: microsoft-learn, 2026-08-03) stamp at line 129 is kept unchanged and its scope was checked explicitly rather than left implicit: it now vouches for a complete seven-member enumeration re-verified against the live source on the date it already carries. Post-edit sweep of both regions found the surrounding prose coherent; the paraphrase drift it did surface is booked as idx-26d, not folded in."
    },
    {
      "id": "idx-26b",
      "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
      "class": "replacement",
      "status": "resolved",
      "raised": "2026-08-03",
      "summary": "Surfaced by the post-edit file sweep for idx 26, in the same compliance-mapping table as idx 26's second locator, but outside both of its anchors — so booked separately rather than folded in (gap discipline; operator-ratified 2026-08-03). The Accuracy metrics row attributes 'Quantitative analyses' to the Responsible AI Scorecard. That is not a scorecard segment name: how-to-responsible-ai-scorecard calls the corresponding segment 'model performance'. The defect is a cross-attribution between two different standards rather than mere imprecision — 'Quantitative Analyses' is a canonical Model Card section, and this same file lists it as one at line 77. Repair is a replacement, so it is outside the delete-only envelope. Whether the right fix is to rename the segment or to drop the row is not yet decided.",
      "evidence": "docs/r11-pilot-results.md §9.6; https://learn.microsoft.com/azure/machine-learning/how-to-responsible-ai-scorecard",
      "anchors": [
        "| **Accuracy metrics** | Responsible AI Scorecard: Quantitative analyses |"
      ],
      "resolution": "Operator-ratified 2026-08-03, form (a) - rename rather than drop the row. The source page was re-fetched live this session (how-to-responsible-ai-scorecard) rather than taken from §9.6 as a premise, and it names the segment model performance: \"The model performance segment displays your model most important metrics and characteristics of your predictions and how well they satisfy your desired target values.\" The Accuracy metrics row now reads \"Responsible AI Scorecard: model performance\". Rename was chosen over deletion because the EU AI Act accuracy-metrics mapping is genuine and source-supported; dropping the row would have removed true information to repair a naming defect. Line 77 keeps Quantitative analyses as a Model Card section, which is correct there and is what made the cross-attribution visible. The **Confidence:** Verified (Baseline + MCP-inferred) stamp at the foot of the compliance table was deliberately NOT upgraded: it covers all six rows and only one was measured against the source this session, so re-dating or strengthening it would have extended a verification claim over unmeasured content - the §9.7 defect class."
    },
    {
      "id": "idx-27",
      "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
      "class": "replacement",
      "status": "open",
      "raised": "2026-08-03",
      "summary": "Failed out of O2 on cond 3 (§9.6): the cited source DOES establish the mechanism — faqs-generative-orchestration states 'Makers can require user confirmation before executing tools that modify data' — so deleting the Plugin-actions row would destroy source-confirmed information. The defect is modality, not fabrication: the file presents confirmation prompts as a built-in disclosure, whereas the source makes them maker-configured. The Chat-interface row in the same table is imprecise for the same reason: the FAQ documents a default transparency message ('Just so you are aware, I sometimes use AI to answer your questions.'), not a 'Powered by AI' badge. Repair is a replacement, outside the delete-only envelope.",
      "evidence": "docs/r11-pilot-results.md §9.6; https://learn.microsoft.com/microsoft-copilot-studio/faqs-generative-orchestration",
      "anchors": [
        "| **Plugin actions** | Confirmation prompts før sensitive actions (send email, delete file) |",
        "| **Chat interface** | \"Powered by AI\" badge i chat window |"
      ]
    },
    {
      "id": "idx-36",
      "file": "skills/ms-ai-security/references/ai-security-engineering/ai-threat-modeling-stride.md",
      "class": "multi-locator",
      "status": "open",
      "raised": "2026-08-03",
      "summary": "Applying idx 36 alone yields a severity table more precise than the prose that cites it, so a companion edit is required for the prose to match the narrowed table. Held back from 957ebef as out of envelope.",
      "evidence": "docs/r11-pilot-results.md §9.4, §9.5",
      "anchors": [
        "Øker severity bar; krever mer robust adversarial defenses"
      ]
    },
    {
      "id": "idx-18",
      "file": "skills/ms-ai-engineering/references/rag-architecture/rag-caching-optimization.md",
      "class": "multi-locator",
      "status": "open",
      "raised": "2026-08-03",
      "summary": "No reduction exists. The surviving claim is a whole titled section on Azure AI Search built-in caching plus a **Verified** row in the verification table, so no deletion confined to a single locator can repair the file. This is the member that most clearly motivated G7.",
      "evidence": "docs/r11-pilot-results.md §9.3, §9.4",
      "anchors": [
        "**Automatic Caching Behavior:**",
        "| Azure AI Search caching | **Verified** | Microsoft Learn docs (4, 6) |"
      ]
    },
    {
      "id": "idx-26d",
      "file": "skills/ms-ai-governance/references/responsible-ai/transparency-documentation-standards.md",
      "class": "replacement",
      "status": "open",
      "raised": "2026-08-03",
      "summary": "Surfaced by the post-edit sweep for idx-26c. The five original members of the scorecard component list carry paraphrased names rather than the source segment names, and idx-26c added two members that DO carry source names, so the list is now mixed. Concretely: item 5 Data quality maps to the source segment data analysis; item 3 Model interpretability maps to top important factors; item 1 Model overview is described as \"Architecture, training data, intended use\", which is Model Card content - the source summary segment is a model overview plus the key target values the user set. That last one is the same cross-attribution class as idx-26b rather than mere imprecision, since Model details / Intended use / Training data are canonical Model Card sections listed in this same file at lines 71-76. Full canonicalisation was offered to the operator as part of the idx-26c decision and was NOT chosen; the ratified scope there was completeness only, so this is booked rather than folded in. Open choice: rename the five to the source segment names in source order, or rewrite item 1 alone (the only member where the drift produces a false attribution rather than a recognisable paraphrase) and leave the rest.",
      "evidence": "docs/r11-pilot-results.md §9.8; https://learn.microsoft.com/azure/machine-learning/how-to-responsible-ai-scorecard",
      "anchors": [
        "1. **Model overview**: Architecture, training data, intended use"
      ]
    }
  ]
}