{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002161919,0.0001201573,0.0001554542,0.0002159067,0.0002477302,0.00003862632,0.0003608462,0.00006246674,0.00006029054],"category_scores_gemma":[0.0002191843,0.0001318298,0.00001314695,0.0003455416,0.0001730667,0.0005732974,0.00023646,0.0001485347,0.00002371914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111865,"about_ca_system_score_gemma":0.0001818606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003132462,"about_ca_topic_score_gemma":0.0002010664,"domain_scores_codex":[0.9981733,0.0001864704,0.0002841518,0.0006559992,0.000308928,0.0003911684],"domain_scores_gemma":[0.9991434,0.0001024938,0.0000928712,0.0004277836,0.0000179559,0.0002155552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004451853,0.00021142,0.09640415,0.00006626369,0.00003885209,0.000005226722,0.006608481,0.000252688,0.000009855519,0.002491018,0.3178097,0.5760579],"study_design_scores_gemma":[0.0005118356,0.0001058297,0.07054387,0.00001419226,0.000002354565,0.000001316195,0.03435624,0.01002166,0.000001889364,0.001536207,0.882758,0.0001465744],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1508057,0.002066233,0.181461,0.6300251,0.002084322,0.008796184,0.003781683,0.0005055415,0.02047426],"genre_scores_gemma":[0.9219433,0.01675791,0.0327073,0.01939943,0.0004943381,0.00186552,0.004400575,0.00009927951,0.002332318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7711377,"threshold_uncertainty_score":0.5375863,"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."}}