{"id":"W4377220609","doi":"10.3389/fpubh.2023.1154595","title":"International perspective on military exposure data sources, applications, and opportunities for collaboration","year":2023,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; Veterans Affairs Canada","funders":"U.S. Department of Veterans Affairs; Government of Canada; U.S. Department of Defense","keywords":"Leverage (statistics); Documentation; Military personnel; Work (physics); Perspective (graphical); Public relations; Political science; Environmental health; Medicine; Business; Engineering; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09060453,0.0009952304,0.0009706425,0.008057219,0.00350819,0.01731705,0.002363711,0.006055397,0.01248769],"category_scores_gemma":[0.04827956,0.0007552922,0.001456757,0.01406908,0.007898849,0.02520722,0.01174297,0.01279819,0.001888373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004824815,"about_ca_system_score_gemma":0.0154078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005358557,"about_ca_topic_score_gemma":0.004524096,"domain_scores_codex":[0.9687535,0.01899604,0.003218311,0.003125449,0.003991214,0.001915393],"domain_scores_gemma":[0.8439296,0.1088633,0.005814548,0.009788865,0.02326221,0.008341451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001548333,0.0001461047,0.006945234,0.003071315,0.0001157675,0.0008256165,0.01655553,0.001232805,0.001301101,0.5230249,0.1455249,0.3011019],"study_design_scores_gemma":[0.00001209444,0.00006783826,0.003329124,0.004611917,0.00002990099,0.0005800719,0.01294572,0.0003569407,0.0003351281,0.06617595,0.9115015,0.00005376468],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.008484715,0.1962544,0.04085944,0.646263,0.01123816,0.0001977436,0.001702849,0.0003053309,0.09469447],"genre_scores_gemma":[0.2558976,0.4229673,0.1511179,0.1148612,0.02758676,0.001054311,0.004840059,0.0006364122,0.02103853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09060453,"threshold_uncertainty_score":0.4791683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08173858249427697,"score_gpt":0.3262046099262881,"score_spread":0.2444660274320111,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}