{
  "id": "AIBD-0584",
  "title": "SpeciesNet supports ecological estimates similar to expert analysis for shared species, with important omissions",
  "summary": "Researchers used MegaDetector and SpeciesNet to produce ecological estimates similar to expert analysis for many species, while some species were missed or misclassified.",
  "date_observed": "2026-03-17",
  "date_basis": "source publication",
  "date_precision": "day",
  "benefit_status": "reported",
  "source_collection": null,
  "source_entry": null,
  "account": "Thornton, Morris, and colleagues compared ecological occupancy models derived from automated SpeciesNet identifications with models derived from expert identifications. The study used 638 cameras in Washington, 263 in Montana, and 326 in Guatemala. MegaDetector 5a located animals, and SpeciesNet classified the crops, with regional species lists, taxonomic adjustments, and sequence processing. Across six AI configurations, mean absolute differences in occupancy probabilities were 0.04-0.05 in Washington, 0.05-0.07 in Montana, and 0.03-0.06 in Guatemala. These comparisons cover species present in both datasets; they are not image-accuracy percentages. Some individual species differed substantially, with a maximum difference of 0.40 in Montana. All 94 mountain-goat detections in Montana were classified as domestic goats and omitted from the AI occupancy models. Montana's expert workflow already used MegaDetector for initial sorting. The study demonstrates automated ecological analysis with broadly similar results for shared species, while preserving a need for human review in some research tasks. It does not measure net labor savings, changed management decisions, or wildlife outcomes. Google developer Dan Morris coauthored the study. The author summary was first dated March 17, 2026; the full paper appeared on May 6.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "SpeciesNet identifies wildlife in images. The study tested whether automated identifications could support ecological occupancy models without routine human verification of each image.",
  "system": "MegaDetector and SpeciesNet",
  "model_version": "MegaDetector 5a; SpeciesNet checkpoint not reported",
  "developer": "Google and MegaDetector contributors",
  "deployer": "Washington State University and collaborating wildlife research teams",
  "location": "Washington and Montana, United States; Maya Biosphere Reserve, Guatemala",
  "domain": "Environment and conservation",
  "segment": "Ecological occupancy modeling from camera images",
  "benefit_type": "Improved Knowledge Discovery",
  "taxonomy_note": "The observed result is useful ecological modeling with automated species identifications. Faster research and conservation improvements are not measured outcomes in this study.",
  "registry_ids": {},
  "ratings": [
    {
      "group": "organizational",
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      "reason": "The study demonstrates an automated analytical capability with broadly similar occupancy estimates for shared species. Rare-species omissions and setting-specific differences limit its use."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "No changed conservation decision or wildlife outcome is documented."
    }
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  "ai_role": {
    "status": "assessed",
    "statement": "MegaDetector located animals and SpeciesNet supplied identifications for ecological models. Researchers configured and analyzed the workflow. The comparison tests analytical usefulness, not a measured improvement in conservation outcomes or net labor time."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Thornton D, Morris D, King T, Perera-Romero L, Anderson A, Garcia-Anleu R, Fitkin S, Vynne C. Identification of camera trap images by artificial intelligence and human experts produces similar multi-species occupancy models. Journal of Applied Ecology 63(5):e70370. First published May 6, 2026. doi:10.1111/1365-2664.70370.",
      "title": "Identification of camera trap images by artificial intelligence and human experts produces similar multi-species occupancy models",
      "url": "https://doi.org/10.1111/1365-2664.70370",
      "date": "2026-05-06",
      "written_by": "makers",
      "quote": null
    },
    {
      "kind": "dataset",
      "citation": "Archived code and data accompanying the Thornton et al. occupancy study. Zenodo record 19560799, version 1.0.1, April 13, 2026.",
      "title": "Code and data for the SpeciesNet occupancy comparison, version 1.0.1",
      "url": "https://zenodo.org/records/19560799",
      "date": "2026-04-13",
      "written_by": "makers",
      "quote": null,
      "supports_benefit": false
    }
  ],
  "replication": {
    "produced_by": [
      "Camera datasets from Washington (2016-2017), Montana (2018-2021), and Guatemala (2018-2019).",
      "MegaDetector 5a detections at confidence 0.15 or above, SpeciesNet classifications, regional filters, and six processing configurations.",
      "Comparison of ecological models fitted to AI and expert identifications of the same images."
    ],
    "must_hold": [
      "The system must retain the species that matter to the intended ecological question.",
      "The exact SpeciesNet checkpoint and processing settings must be recoverable.",
      "Regional input data and analysis scripts must be available, with appropriate protections for sensitive wildlife locations."
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "SpeciesNet supports ecological estimates with some species missed",
  "metric": "Mean occupancy differences of 0.03-0.07 across study areas",
  "comparator": {
    "against": "Ecological models fitted to expert identifications of the same images; Montana experts already used MegaDetector for initial sorting.",
    "before": "Expert-based estimates of species occupancy and species-environment relationships.",
    "after": "Mean absolute occupancy-probability differences of 0.04-0.05 in Washington, 0.05-0.07 in Montana, and 0.03-0.06 in Guatemala for shared species.",
    "basis": "measured",
    "quote": "We found that for most species of mammal, AI based models were remarkably similar to expert-based models",
    "url": "https://doi.org/10.1111/1365-2664.70370"
  },
  "later": {
    "status": "unknown",
    "date": null,
    "note": "Subsequent deployment, correction of the reported errors, and changes to conservation decisions have not been established by this source review.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-25",
      "change": "Added the original comparative study, its methods and limitations, source lineage, and unresolved follow-up questions.",
      "source": "https://doi.org/10.1111/1365-2664.70370",
      "editor": "AIBD"
    }
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  "claim_basis": "Sources: the makers' peer-reviewed study and the makers' dataset. For the benefit itself, the cited sources include no independent source and no finding by a regulator or court.",
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          "answer": "The other new conservation record concerns a separate evaluation that also uses MegaDetector. It is not a replication of this result."
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      "economics[].valuation_method": {
        "state": "not_reported",
        "review_state": "reviewed",
        "coverage": "complete",
        "family": "monetary observations"
      },
      "evaluations[].ai_uplift": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "The comparison demonstrates an automated route to useful ecological estimates for many shared species. It does not quantify net time saved or improved conservation decisions relative to the existing workflow."
        },
        "provenance": {
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        "review_state": "reviewed",
        "coverage": "complete"
      },
      "evaluations[].comparator": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Ecological models fitted to expert identifications of the same images; Montana experts already used MegaDetector for initial sorting."
        },
        "provenance": {
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        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "evaluations[].design": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Comparative ecological analysis of the same regional image datasets using expert labels and six AI processing configurations. The comparator is not uniformly free of AI."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "evaluations[].effect": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Mean occupancy estimates were broadly similar for shared species. Individual differences reached 0.40 in Montana, and some species were omitted or added by classification errors."
        },
        "provenance": {
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        "coverage": "complete"
      },
      "evaluations[].exposure": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "The study includes 638 Washington cameras, 263 Montana cameras, and 326 Guatemala cameras. These are sampling units, not beneficiary counts or a denominator for conservation success."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "evaluations[].id": {
        "state": "value_present",
        "value": {
          "kind": "text",
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        "provenance": {
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        "review_state": "reviewed",
        "coverage": "complete"
      },
      "evaluations[].measurement_method": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "The authors compared occupancy probabilities, detection probabilities, species-environment relationships, precision, and spatial projections from fitted models."
        },
        "provenance": {
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        "review_state": "reviewed",
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      "evaluations[].metric": {
        "state": "value_present",
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          "kind": "structured",
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            "mean_absolute_occupancy_probability_difference": {
              "Washington": [
                0.04,
                0.05
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              "Montana": [
                0.05,
                0.07
              ],
              "Guatemala": [
                0.03,
                0.06
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            },
            "scope": "Species shared by expert and AI datasets, across six processing configurations.",
            "maximum_individual_species_difference_Montana": 0.4,
            "Montana_mountain_goat_detections_misclassified": 94
          }
        },
        "provenance": {
          "kind": "source_review",
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete",
        "display": {
          "answer": "Mean absolute occupancy-probability differences were 0.04-0.05 in Washington, 0.05-0.07 in Montana, and 0.03-0.06 in Guatemala for species shared by the AI and expert datasets."
        }
      },
      "evaluations[].reproduction_materials": {
        "state": "value_present",
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          "kind": "structured",
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            "available": "Washington CSV inputs and R analysis scripts for all three regions.",
            "missing": "Montana and Guatemala CSV inputs referenced by the scripts were absent from the inspected release.",
            "public_image_subset": {
              "url": "https://lila.science/datasets/wsu-lynx/",
              "locations": 637,
              "images": 1205453,
              "qualification": "This public subset excludes humans and vehicles and differs from the paper's Washington denominator."
            },
            "supplement": {
              "url": "https://besjournals.onlinelibrary.wiley.com/doi/suppl/10.1111/1365-2664.70370/supinfo/jpe70370-sup-0001-Supinfo.docx",
              "access": "The complete DOCX could not be retrieved; only the article's description and captions were readable."
            }
          }
        },
        "provenance": {
          "kind": "source_review",
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete",
        "display": {
          "answer": "Zenodo version 1.0.1 contains Washington CSV inputs and scripts for all regions. Referenced Montana and Guatemala inputs are absent. The public Washington image subset has a different denominator. The complete supplement was inaccessible."
        }
      },
      "evaluations[].setting_sample": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "Washington": {
              "cameras": 638,
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            },
            "Montana": {
              "cameras": 263,
              "images_approx": 1700000,
              "collection_years": "2018-2021"
            },
            "Guatemala": {
              "cameras": 326,
              "images_approx": 280000,
              "collection_years": "2018-2019"
            }
          }
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
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          ],
          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete",
        "display": {
          "answer": "Washington: 638 cameras and about 1.8 million images. Montana: 263 cameras and about 1.7 million images. Guatemala: 326 cameras and about 280,000 images."
        }
      },
      "evaluations[].uncertainty_limitations": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Comparisons of model estimates cover shared species. Rare-species omissions remain outside those comparisons. Regional filtering and sequence processing affect results. Montana's expert workflow already used AI. The exact SpeciesNet checkpoint, some regional inputs, and the complete supplement were unavailable in this review."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
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          "source_refs": [
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "external_references[]": {
        "state": "value_present",
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          "kind": "structured",
          "data": {
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            "public_washington_subset": "https://lila.science/datasets/wsu-lynx/"
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        },
        "provenance": {
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete",
        "display": {
          "answer": "The study DOI is 10.1111/1365-2664.70370. The record links both archive versions and the public Washington image subset."
        }
      },
      "follow_up[]": {
        "state": "not_assessed",
        "reason": "Post-study adoption and conservation decisions require follow-up.",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "human_oversight": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Researchers supplied regional filters and taxonomic adjustments and assessed the statistical output. Expert identifications provided the comparator. The authors recommend human review for some rare-species and biodiversity tasks."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
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          "source_refs": [
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          "reviewed_on": "2026-10-25"
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        "review_state": "reviewed",
        "coverage": "complete"
      },
      "id": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "AIBD-0584"
        },
        "provenance": {
          "kind": "source_review",
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            "AIBD-0584-source-0001"
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "infrastructure_dependencies[]": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Camera datasets, MegaDetector, SpeciesNet, regional species information, and statistical analysis in R were required. A complete historical SpeciesNet checkpoint and all regional data inputs are not preserved in the inspected archive."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
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          "source_refs": [
            "AIBD-0584-source-0001",
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].beneficiaries": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "The participating ecological research teams. Benefits to monitoring organizations and conservation managers are prospective unless subsequent use is documented."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].beneficiary_count": {
        "state": "not_reported",
        "provenance": {
          "kind": "source_review",
          "basis": "Reviewed the original study and its stated data availability.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
          ],
          "reviewed_on": "2026-10-25"
        },
        "reason": "The study does not count people who benefited. Cameras, images, species, and detection events are study units.",
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].benefit_type": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Improved Knowledge Discovery"
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Original study, methods, results, and discussion.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].claim": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Thornton, Morris, and colleagues compared ecological occupancy models derived from automated SpeciesNet identifications with models derived from expert identifications. The study used 638 cameras in Washington, 263 in Montana, and 326 in Guatemala. MegaDetector 5a located animals, and SpeciesNet classified the crops, with regional species lists, taxonomic adjustments, and sequence processing. Across six AI configurations, mean absolute differences in occupancy probabilities were 0.04-0.05 in Washington, 0.05-0.07 in Montana, and 0.03-0.06 in Guatemala. These comparisons cover species present in both datasets; they are not image-accuracy percentages. Some individual species differed substantially, with a maximum difference of 0.40 in Montana. All 94 mountain-goat detections in Montana were classified as domestic goats and omitted from the AI occupancy models. Montana's expert workflow already used MegaDetector for initial sorting. The study demonstrates automated ecological analysis with broadly similar results for shared species, while preserving a need for human review in some research tasks. It does not measure net labor savings, changed management decisions, or wildlife outcomes. Google developer Dan Morris coauthored the study. The author summary was first dated March 17, 2026; the full paper appeared on May 6."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Original study, methods, results, and discussion.",
          "record_fields": [],
          "source_refs": [
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].distribution": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Agreement varied by region and species. Shared-species comparisons do not cover species missing from the AI datasets. Rare or difficult species may require human review."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
          ],
          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].duration": {
        "state": "not_reported",
        "provenance": {
          "kind": "source_review",
          "basis": "Reviewed the original study and its stated data availability.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
          ],
          "reviewed_on": "2026-10-25"
        },
        "reason": "The study does not establish how long the benefit persisted after the evaluation.",
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].id": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "AIBD-0584-outcome-research"
        },
        "provenance": {
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          "record_fields": [],
          "source_refs": [
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].stakeholder_group": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Participating research organizations. Wider conservation benefits remain unmeasured."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Original study, methods, results, and discussion.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].status": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Reported research result with explicit limitations."
        },
        "provenance": {
          "kind": "source_review",
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          "source_refs": [
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].tradeoffs": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Broad agreement coexisted with substantial errors for some species. All 94 Montana mountain-goat detections were labeled domestic goats and omitted from AI occupancy models. Some species appeared falsely in AI outputs."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
          ],
          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "outcomes[].unexpected": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "The result concerns the intended use of AI to support wildlife-image analysis."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Original study, methods, results, and discussion.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "places[]": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "case_jurisdictions": [
              "United States",
              "Guatemala"
            ],
            "developer_jurisdictions": [
              "United States"
            ],
            "study_places": [
              "Washington, United States",
              "Glacier National Park, Montana, United States",
              "Maya Biosphere Reserve, Guatemala"
            ],
            "location_label": "Study locations"
          }
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
          "record_fields": [],
          "source_refs": [
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          ],
          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete",
        "display": {
          "answer": "Washington and Montana in the United States, and the Maya Biosphere Reserve in Guatemala."
        }
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "SpeciesNet identifies wildlife in images. The study tested whether automated identifications could support ecological occupancy models without routine human verification of each image."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Original study, methods, results, and discussion.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001"
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "record_dates": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "image_collection": {
              "Washington": "2016-2017",
              "Montana": "2018-2021",
              "Guatemala": "2018-2019"
            },
            "first_dated_public_account": "2026-03-17",
            "code_release_1_0_0": "2026-03-17",
            "code_and_data_release_1_0_1": "2026-04-13",
            "full_paper_publication": "2026-05-06",
            "author_summary_modified": "2026-05-07",
            "case_date": "2026-03-17",
            "case_date_basis": "Earliest dated author account of this work. The original March text was not recovered; the current summary was modified in May.",
            "last_reviewed": "2026-10-25"
          }
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Thornton et al., sections 2.1-2.6, Table 1, sections 3.1 and 4, author contributions and disclosure; versioned archive and source chronology where specified.",
          "record_fields": [],
          "source_refs": [
            "AIBD-0584-source-0001",
            "AIBD-0584-source-0002",
            "https://appliedecologistsblog.com/2026/03/17/review-of-camera-trap-images-by-artificial-intelligence-produce-accurate-models-for-rapid-decision-making/",
            "https://zenodo.org/records/19073128"
          ],
          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete",
        "display": {
          "answer": "Images were collected in 2016-2021 across the three regions. An author account was first dated March 17, 2026; the full study appeared May 6. The original March text was not recovered."
        }
      },
      "replication.attempts[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "The system must retain the species that matter to the intended ecological question. The exact SpeciesNet checkpoint and processing settings must be recoverable. Regional input data and analysis scripts must be available, with appropriate protections for sensitive wildlife locations."
        },
        "provenance": {
          "kind": "source_review",
          "basis": "Original study, methods, results, and discussion.",
          "record_fields": [],
          "source_refs": [
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          "reviewed_on": "2026-10-25"
        },
        "review_state": "reviewed",
        "coverage": "complete"
      },
      "revision_history[]": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "revisions": [
              {
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