{
  "headline": {
    "risk_accuracy": 0.888,
    "risk_macro_f1": 0.8660594730892965,
    "critical_recall": 0.92,
    "worktype_accuracy": 1.0,
    "simops_n_pairs": 260,
    "simops_conflict_rate": 0.5
  },
  "methodology": "Risk and work-type labels for ML evaluation come from the deterministic rule engine applied to structured fields (not from free text). Classifiers see only concatenated FA+EN free text. SIMOPS is the same rule engine that generated the pair labels, so precision/recall would be circular; we report pair balance instead.",
  "eval_results": {
    "protocol": {
      "honesty": "100% synthetic bilingual Permit-to-Work corpus. No real permits, incidents, PII, or named facilities. Zones/equipment tags are fictional and partly reused from the PetroSafe RAG demo unit for portfolio consistency. About 20% of permits deliberately mismatch free text vs structured flags to simulate documentation gaps.",
      "n_permits": 620,
      "n_train": 495,
      "n_test": 125,
      "text_flag_mismatch_rate": 0.19193548387096773,
      "rule_noncompliant_fraction": 0.3419354838709677,
      "n_simops_pairs": 260
    },
    "risk_classifier": {
      "accuracy": 0.888,
      "macro_f1": 0.8660594730892965,
      "confusion_matrix": {
        "labels": [
          "low",
          "medium",
          "high",
          "critical"
        ],
        "matrix": [
          [
            30,
            0,
            0,
            0
          ],
          [
            1,
            47,
            4,
            4
          ],
          [
            1,
            1,
            11,
            1
          ],
          [
            0,
            1,
            1,
            23
          ]
        ]
      },
      "per_class": {
        "low": {
          "precision": 0.9375,
          "recall": 1.0,
          "n": 30
        },
        "medium": {
          "precision": 0.9591836734693877,
          "recall": 0.8392857142857143,
          "n": 56
        },
        "high": {
          "precision": 0.6875,
          "recall": 0.7857142857142857,
          "n": 14
        },
        "critical": {
          "precision": 0.8214285714285714,
          "recall": 0.92,
          "n": 25
        }
      },
      "critical_recall": 0.92
    },
    "worktype_classifier": {
      "accuracy": 1.0,
      "macro_f1": 1.0
    },
    "simops_conflict_detector": {
      "n_pairs": 260,
      "n_conflict": 130,
      "n_no_conflict": 130,
      "conflict_rate": 0.5,
      "rule_breakdown": {
        "SIMOPS-01": 26,
        "SIMOPS-02": 26,
        "SIMOPS-03": 26,
        "SIMOPS-04": 26,
        "SIMOPS-05": 26
      },
      "note": "SIMOPS conflict checking is a deterministic rule engine (src/permitguard/rules.py), not a learned model -- there is no separate ground truth to score it against, so precision/recall would be a circular 100%/100%. The numbers above (pair balance + which of the 5 rules fired) demonstrate the engine covers a realistic, balanced mix of conflict and non-conflict scenarios, including 'mitigated near-miss' cases."
    },
    "published_baselines": [
      {
        "method": "ContractGuard hybrid TF-IDF + Logistic Regression (Aria AI sibling product)",
        "finding": "Site claim: 92% clause-classification accuracy and 100% recall on high-risk clauses, on a synthetic bilingual contract-clause corpus.",
        "citation": "Aria AI ContractGuard product page / Hugging Face Space contractguard-clause-analyzer. https://aria-ai.ir"
      },
      {
        "method": "NLP hazard identification from construction/safety text (public literature)",
        "finding": "Text classifiers are used to flag missing PPE/hazard statements in permits and JSAs; reported performance is corpus-specific and not a universal PTW KPI.",
        "citation": "Goh, Y.M. & Ubeynarayana, C.U. (2017). Construction accident narrative classification: An automatic text mining approach. Safety Science 99:70-80. https://doi.org/10.1016/j.ssci.2017.04.001"
      }
    ],
    "pipeline_seconds": 2.7
  }
}