{"id":"W6992340513","doi":"","title":"Les déterminants du comporement des agents dans la demande du tabac à travers les provinces du Canada","year":2006,"lang":"fr","type":"other","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Population; Agency (philosophy); Legislation","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":[0.0003648886,0.0002076272,0.000243531,0.001018831,0.001418357,0.001729357,0.0006653566,0.0003491319,0.01743368],"category_scores_gemma":[0.002462439,0.000212514,0.0003906726,0.002522237,0.0003750201,0.0004330032,0.0004539185,0.0007214603,0.000637849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01484099,"about_ca_system_score_gemma":0.03642887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951637,"about_ca_topic_score_gemma":0.9960994,"domain_scores_codex":[0.9995698,0.00004884609,0.00001413446,0.00004064879,0.0001258629,0.0002006054],"domain_scores_gemma":[0.9984106,0.0003678361,0.0001965268,0.00004435619,0.0006711975,0.0003094933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004411494,0.0001310099,0.8778146,0.0002117035,0.0002336993,0.0003814878,0.003385456,0.005442705,0.0009970403,0.02111528,0.04165863,0.04818725],"study_design_scores_gemma":[0.00004426241,0.00003448962,0.9471375,0.0001022519,0.00009503782,0.00008076239,0.005225611,0.004768457,0.0008190839,0.0009734745,0.04069018,0.00002894049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9094638,0.002114973,0.00106394,0.005554215,0.00004442507,0.0000792731,0.03916249,0.0001094268,0.04240734],"genre_scores_gemma":[0.9276904,0.001220035,0.0008394049,0.0001392275,0.00001415007,0.0000343705,0.004766563,0.00003385422,0.06526193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01743368,"threshold_uncertainty_score":0.1076794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006678628922958548,"score_gpt":0.1679487966173096,"score_spread":0.1612701676943511,"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."}}