{"id":"W191436273","doi":"10.71781/23160","title":"Analyse et explication de la variation du taux d’homicide en Europe","year":2011,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Census and Population Estimation","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Montréal","keywords":"Homicide; Per capita; Statistics; Econometrics; Demography; Geography; Mathematics; Poison control; Injury prevention; Sociology; Medicine; Population","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002377133,0.0003807855,0.0004567232,0.0002116383,0.0002372606,0.0004416822,0.0005456277,0.0005489627,0.005680515],"category_scores_gemma":[0.00119909,0.0004185506,0.0001246132,0.0003841796,0.00003580212,0.0006614682,0.0001007222,0.0003849328,0.001562336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001965145,"about_ca_system_score_gemma":0.0004659843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002255935,"about_ca_topic_score_gemma":0.001313341,"domain_scores_codex":[0.9969771,0.001108182,0.0008879277,0.000525153,0.000234427,0.0002672079],"domain_scores_gemma":[0.9970328,0.0007338246,0.00112668,0.0005533469,0.0004242492,0.0001291268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007358034,0.002495041,0.05722677,0.0006442982,0.0006694599,0.00004193401,0.2510449,0.004908335,0.02226156,0.275265,0.002718119,0.3819888],"study_design_scores_gemma":[0.001937057,0.0001690847,0.7727768,0.0009488325,0.002061504,0.00008365344,0.002131511,0.09110528,0.009611598,0.02273672,0.09494853,0.001489361],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8411648,0.00008381537,0.040346,0.0003691373,0.0004652105,0.001409733,0.0001084705,0.00001126523,0.1160416],"genre_scores_gemma":[0.762223,0.0002853739,0.2033072,0.0001056549,0.0003599897,0.0001407696,0.007151945,0.0001527551,0.02627332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7155501,"threshold_uncertainty_score":0.9998266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06293022757547147,"score_gpt":0.3780420918378646,"score_spread":0.3151118642623932,"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."}}