{"id":"W7043652090","doi":"","title":"Synthèse des données probantes: Veiller à ce que les décisions liées à la santé touchant le personnel militaire canadien, les anciens combattants et leurs familles soient fondées sur les meilleures données probantes disponibles","year":2022,"lang":"fr","type":"other","venue":"","topic":"X-ray Diffraction in Crystallography","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Excellence; Military government; Work (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03133104,0.001440484,0.00155411,0.01355121,0.001793602,0.006099181,0.002159774,0.00231446,0.0595503],"category_scores_gemma":[0.1756407,0.001057876,0.003503896,0.01019946,0.001744465,0.002514303,0.003036113,0.002559297,0.01476338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004983201,"about_ca_system_score_gemma":0.03809733,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0937872,"about_ca_topic_score_gemma":0.1268226,"domain_scores_codex":[0.9765111,0.006934812,0.005853252,0.001864769,0.008332745,0.0005032439],"domain_scores_gemma":[0.8813449,0.06927823,0.00505389,0.00939285,0.03374079,0.00118929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0009588628,0.0002091784,0.005200185,0.05863349,0.00184221,0.001290232,0.008151888,0.0009921187,0.005731457,0.01107439,0.6199679,0.2859482],"study_design_scores_gemma":[0.0001227971,0.00004873626,0.002857449,0.01706835,0.0006904405,0.000314158,0.001723715,0.0001109672,0.001909621,0.001758866,0.9733213,0.00007357699],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"review","genre_scores_codex":[0.01662791,0.1350993,0.04210417,0.04138136,0.008418149,0.005276724,0.6523192,0.003240372,0.09553283],"genre_scores_gemma":[0.08964323,0.1303918,0.2295746,0.01725889,0.001875903,0.0120396,0.4475282,0.003717183,0.06797063],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9062128,"threshold_uncertainty_score":0.1992156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0642555344616497,"score_gpt":0.2728891703635865,"score_spread":0.2086336359019368,"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."}}