{"id":"W2104551107","doi":"","title":"COMPAS: UN MODÈLE DE MICROSIMULATION SANTÉ POUR LE QUÉBEC","year":2014,"lang":"fr","type":"article","venue":"Cahiers de recherche","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Microsimulation; Political science; Physics; Philosophy","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"],"consensus_categories":[],"category_scores_codex":[0.0179942,0.0002578727,0.0003649874,0.0002391485,0.0004004088,0.0002980196,0.0005934804,0.001243219,0.0002646332],"category_scores_gemma":[0.007880039,0.0002596184,0.0002601226,0.0008967661,0.00044444,0.0002971875,0.0000428749,0.001259769,0.0005146351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159455,"about_ca_system_score_gemma":0.001719378,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007666762,"about_ca_topic_score_gemma":0.001300421,"domain_scores_codex":[0.9927241,0.004921985,0.0006468041,0.0005657918,0.0004897157,0.0006515681],"domain_scores_gemma":[0.9918797,0.00657075,0.0002705693,0.0005904672,0.0003586316,0.0003298951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007272755,0.0002294411,0.002463245,0.00004429948,0.00006220002,0.000006130411,0.01251834,0.100177,0.01596022,0.02241697,0.007056334,0.8389931],"study_design_scores_gemma":[0.0006518692,0.00005687165,0.001725418,0.00007726374,0.0000633563,0.00002571369,0.00319595,0.7256883,0.003130241,0.2252883,0.03982231,0.0002744517],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3444577,0.004278725,0.627355,0.008096023,0.0003699108,0.0001201257,0.000009487904,0.00007296736,0.01524013],"genre_scores_gemma":[0.8637896,0.0005799583,0.107227,0.001641708,0.0002397623,0.00001302296,0.00001085992,0.00004003859,0.02645799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8387187,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3162265275002829,"score_gpt":0.416247567668811,"score_spread":0.1000210401685282,"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."}}