{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.01326",
  "title": "An ontology for cross-sectoral crisis management: core and public health modules",
  "summary": "ECMO replaces ad hoc, inconsistent data schemas for crisis information with a formal modular OWL ontology (core plus public health module aligned with SNOMED CT and ICD-11) that constrains how unstructured epidemiological news is populated into a knowledge graph. The cost is ontology-engineering effort, OWL tooling and reasoning infrastructure, and the complexity of maintaining module alignments, none of which map to GPU or API budgets. It can fail through poor domain coverage for non-health crises, mismatch with existing data pipelines, and the demonstration remaining limited to a single JRC system with no quantified accuracy or interoperability benchmarks.",
  "rationale": "The paper is a knowledge-representation resource rather than an inference or training method, and its qualitative 'initial results' demonstration gives an ML engineer with constrained infrastructure no validated production gain to adopt without domain-specific need.",
  "technique": "Modular OWL ontology for crisis knowledge graphs",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01326",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ECMO replaces ad hoc, inconsistent data schemas for crisis information with a formal modular OWL ontology (core plus public health module aligned with SNOMED CT and ICD-11) that constrains how unstructured epidemiological news is populated into a knowledge graph. The cost is ontology-engineering effort, OWL tooling and reasoning infrastructure, and the complexity of maintaining module alignments, none of which map to GPU or API budgets. It can fail through poor domain coverage for non-health crises, mismatch with existing data pipelines, and the demonstration remaining limited to a single JRC system with no quantified accuracy or interoperability benchmarks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01326",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "next_check": "",
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
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}