{"id":"W8145691","doi":"","title":"Méthodes de modélisation bayésienne et applications en recherche clinique","year":2010,"lang":"en","type":"dissertation","venue":"Minerva Pediatrica","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Université de Sherbrooke","keywords":"Humanities; Philosophy; Political 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":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00230851,0.0003246987,0.0003124866,0.00026085,0.0001271739,0.0002364943,0.001327267,0.001217148,0.0000227658],"category_scores_gemma":[0.000649324,0.0003292531,0.0001504735,0.0006725756,0.00001860287,0.0003203592,0.00008669861,0.001711358,0.0001112333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012016,"about_ca_system_score_gemma":0.001076958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001539846,"about_ca_topic_score_gemma":0.0002717954,"domain_scores_codex":[0.997486,0.0006209061,0.0004909929,0.0007120824,0.000300117,0.0003899261],"domain_scores_gemma":[0.9972313,0.001083061,0.0003253657,0.0008523291,0.0003119851,0.0001959323],"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":[0.00004693129,0.000603601,0.0004924675,0.0004919192,0.00006405794,0.00001752492,0.01120079,0.001909259,0.01273282,0.1522999,0.00934647,0.8107942],"study_design_scores_gemma":[0.0006474364,0.0002309058,0.004031111,0.0001228446,0.0002746011,0.00004887352,0.0003379617,0.6490893,0.003140244,0.3220617,0.01785601,0.002158998],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006516795,0.000439155,0.9830542,0.001136194,0.0003822307,0.0003675183,0.00001627521,0.0003715996,0.007716075],"genre_scores_gemma":[0.07004704,0.001590729,0.9192383,0.0005576703,0.001164177,0.0007266138,0.0006114033,0.0000641523,0.005999863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8086352,"threshold_uncertainty_score":0.999916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1216522617540441,"score_gpt":0.400359408420188,"score_spread":0.2787071466661438,"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."}}