{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts"],"category_scores_codex":[0.003050672,0.003023931,0.002871381,0.002379474,0.006503669,0.001333383,0.003240623,0.001385064,0.0222706],"category_scores_gemma":[0.003877147,0.00256723,0.001426565,0.001910603,0.006998062,0.001339237,0.00171028,0.002009317,0.0001125897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145745,"about_ca_system_score_gemma":0.002806166,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1201672,"about_ca_topic_score_gemma":0.5222254,"domain_scores_codex":[0.9838207,0.003805547,0.002499201,0.003899711,0.002390835,0.003584011],"domain_scores_gemma":[0.9884728,0.00544432,0.001635027,0.002225042,0.001066386,0.001156484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","study_design_scores_codex":[0.002019554,0.01430913,0.1583053,0.02435694,0.004310312,0.002667504,0.14207,0.009962082,0.2521551,0.2298271,0.1318825,0.02813451],"study_design_scores_gemma":[0.003138734,0.001648066,0.05430533,0.0123917,0.0008730122,0.0009138497,0.457521,0.001046885,0.004968734,0.00323569,0.4531441,0.006812934],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8901972,0.06635508,0.001255955,0.002507427,0.002683596,0.003846522,0.005181747,0.001376347,0.0265961],"genre_scores_gemma":[0.9021007,0.04130729,0.007300602,0.0004590553,0.0005487552,0.001863843,0.0003450701,0.001207802,0.04486691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4020581,"threshold_uncertainty_score":0.9999114,"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."}}