{"id":"W1515975278","doi":"10.7202/010853ar","title":"Intérêt de l’analyse des causes multiples dans l’étude de la mortalité aux grands âges : l’exemple français","year":2005,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.004687158,0.001078189,0.001101036,0.00145106,0.003104916,0.00055126,0.001719299,0.001266195,0.0002988522],"category_scores_gemma":[0.0007406736,0.001214802,0.001614825,0.004910214,0.01525461,0.0009570015,0.0001864096,0.001399616,0.00007165483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247935,"about_ca_system_score_gemma":0.0009572217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3554764,"about_ca_topic_score_gemma":0.660926,"domain_scores_codex":[0.990826,0.002312622,0.001242253,0.001298644,0.00123579,0.003084753],"domain_scores_gemma":[0.9956781,0.0009403608,0.0005255194,0.001194783,0.0003513921,0.001309815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006846972,0.0008286388,0.7967753,0.0002678169,0.001558895,0.0002898179,0.1367037,0.001316786,0.0004235767,0.03313954,0.004831424,0.02379603],"study_design_scores_gemma":[0.001444776,0.0001474393,0.770779,0.0002489823,0.001728809,0.00004863336,0.04389992,0.000580596,0.0008341282,0.01113571,0.1676455,0.001506535],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9294981,0.04446549,0.002279777,0.001392602,0.0006669416,0.0007824285,0.0002476207,0.0005277303,0.02013934],"genre_scores_gemma":[0.9488413,0.03993277,0.00678672,0.001806444,0.001099152,0.0002730311,0.0000431242,0.0001715633,0.001045919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3054496,"threshold_uncertainty_score":0.9990302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478179653474134,"score_gpt":0.2891259500342033,"score_spread":0.274344153499462,"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."}}