{"id":"W2610567013","doi":"10.71781/15634","title":"Quelques utilisations de la densité GEP en analyse bayésienne sur les familles de position-échelle","year":2005,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Environmental science","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0006621256,0.0005545055,0.0007896138,0.0008910455,0.005356045,0.0002193299,0.0005924561,0.0009352593,0.0004337165],"category_scores_gemma":[0.0003133258,0.0007677635,0.0007284465,0.0006461305,0.0003516856,0.0006657417,0.0001453659,0.0006311797,0.0002000511],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008168517,"about_ca_system_score_gemma":0.001245287,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2678194,"about_ca_topic_score_gemma":0.09460079,"domain_scores_codex":[0.997077,0.0003177726,0.0008634832,0.0008670659,0.0002125811,0.0006621016],"domain_scores_gemma":[0.9975389,0.0004302631,0.0007968466,0.0005401789,0.0003077642,0.000386089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005183622,0.0008611184,0.677468,0.0002844867,0.002647802,0.001722379,0.06822098,0.03019639,0.005450455,0.201161,0.001301066,0.01016795],"study_design_scores_gemma":[0.001177024,0.00009832965,0.8467292,0.0002963365,0.0012686,0.0006388079,0.04729695,0.0493867,0.003338211,0.002202961,0.04626152,0.00130539],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9223688,0.04120877,0.003190698,0.0006792731,0.000441299,0.0002247881,0.001091666,0.00008911019,0.0307056],"genre_scores_gemma":[0.9526902,0.007760265,0.003746145,0.0002120089,0.0004277216,0.00003329571,0.003088851,0.00005204657,0.0319895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.198958,"threshold_uncertainty_score":0.9994773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008822487101689676,"score_gpt":0.176635513189592,"score_spread":0.1678130260879023,"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."}}