{
  "tool_id": "art-763-ai-displacement-risk-screener",
  "tool_version": "1.0.0",
  "display_name": "AI Displacement Risk Screener",
  "mcp_name": "ai_displacement_risk_screener",
  "mandate_type": "analytics_mandate",
  "wave": 133,
  "gpu": false,
  "url": "https://ainumbers.co/chaingraph/art-763-ai-displacement-risk-screener.html",
  "description": "AI-driven job-displacement risk screener ported from ApexLogics Apex #104 (AL-111). For a task-composition profile (routine cognitive/manual and non-routine analytical/interpersonal/manual shares, 0..100 each), a sector exposure factor, an education level and years in role, the node computes a 0..100 composite displacement score as 0.40 x automation probability + 0.40 x routine-task fraction + 0.20 x sector exposure, clamped and rounded, with an education-adjusted informational score and a four-tier banding (Low <= 30, Medium <= 55, High <= 75, else Critical). Selecting a SOC code looks the occupation up in an embedded 62-row table (title, Frey-Osborne automation probability, task splits, BLS-OES-labelled median salary); leaving it null estimates the probability from the sliders (slope 0.85, floor 0.05, cap 0.99). The node also returns up to three adjacent lower-exposure occupations scored with the caller's sector factor, gated at similarity >= 60. Invalid-domain inputs are rejected by throw with a named reason. The automation probabilities are Frey-Osborne 2013-vintage estimates (stale by construction), the task splits are Apex-embedded approximations, and the median-salary cells labelled BLS OES 2025 stayed ungrounded at porting time (BLS HTTP 403): salary figures are informational only and stay out of the score, with every vintage and grounding flag carried in the kernel metadata (payload DIFFERS.md D1-D4, CITES.md U1-U4).",
  "input_schema_ref": "chaingraph/art-763-ai-displacement-risk-screener.html#manifest",
  "consumes": [],
  "feeds": [],
  "status": "live",
  "conformance_fixtures": true,
  "compute_capability": "server",
  "compute_images": [
    {
      "system": "sha256-source",
      "image_id": "sha256:91fb630e8e4abce5da6b62416996e456f8665988098ae412e7f24786ac07dedc",
      "valid_from": "2026-10-18"
    },
    {
      "system": "risc0",
      "image_id": "sha256:a1a0bc89b5b1febaeda3519f6dbade0fa5ac16beeb143c4e1b01689573567bc6",
      "valid_from": "2026-09-12"
    }
  ],
  "export_capability": [
    "json"
  ],
  "standards_basis": "not_applicable",
  "cited_clause_digest": [],
  "compute_proof_ready": "ready",
  "cycle_preflight": "FAST - 8,306,189 user_cycles max across all 18 fixture vectors, measured exec-only 2026-10-22 (DEFERRED-EXEC-MEASURE-3).",
  "compute_proof": {
    "type": "ZkVmReceipt",
    "system": "risc0",
    "receiptFormat": "groth16-bn254",
    "imageId": "sha256:a1a0bc89b5b1febaeda3519f6dbade0fa5ac16beeb143c4e1b01689573567bc6",
    "seal": "EI8IwG8PcxBQqJDSNkgPiCUQ0nce/ytHb863+jc1oWAjXhEsh/KMM/RMiXm06dmOLrH8CB1/iLjoW4Z8aWkUJyaiWpYz6SPvLFHoAH3bm7UEZ8nkhnAoNsMo5yrNy1FeDpb+o4vZocnRiEanPM58oxxpS86Ey/+rR3HxBTIH2jIBVvPDrWUWMiJxfsVLNNv3ALRVHjV1VWAcFP/hrjDd5wrBs/eU6q+QgsquDeSqBMstdZgB2ZO7VkoGapiiLt7xFBzWpxrCJU5wNmlPZQE2Dz9EYUiPkTxQ34PfuqPeJUISb74Y0p8vHYoTlxk6ZZ7tpjUyiM8RMyRVt8hWMkCJtw==",
    "journal": {
      "chaingraph_version": "0.4.0",
      "kernel_digest": "sha256:91fb630e8e4abce5da6b62416996e456f8665988098ae412e7f24786ac07dedc",
      "output": {
        "adj_score": 11,
        "alts": [
          {
            "salary_delta_vs_selected_usd": 4350,
            "score": 16,
            "sim": 98,
            "soc": "15-1211",
            "title": "Computer/IS Research Scientist"
          },
          {
            "salary_delta_vs_selected_usd": 97030,
            "score": 15,
            "sim": 93,
            "soc": "29-1215",
            "title": "Hospitalist / Physician"
          },
          {
            "salary_delta_vs_selected_usd": 104750,
            "score": 16,
            "sim": 92,
            "soc": "29-1151",
            "title": "Oral & Maxillofacial Surgeon"
          }
        ],
        "automation_pct": 4,
        "median_salary_usd": 132270,
        "prob": 0.04,
        "routine_frac": 0.19,
        "routine_pct": 19,
        "score": 18,
        "sector_exp": 0.45,
        "sector_pct": 45,
        "soc": "15-1132",
        "tier": "Low",
        "title": "Software Developer"
      }
    }
  }
}