{"id":"W7104489099","doi":"10.71781/15763","title":"Modélisation des bi-grappes et sélection des variables pour des données de grande dimension : application aux données d’expression génétique","year":2012,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lebesgue covering dimension; Diaphragm muscle","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003430039,0.0008366485,0.0007587917,0.0004224936,0.001966361,0.001590712,0.001458574,0.0009796044,0.0002147825],"category_scores_gemma":[0.0002931192,0.0007856635,0.0002520503,0.0009310097,0.000404067,0.004191895,0.0004237684,0.0006384459,0.0001124516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954057,"about_ca_system_score_gemma":0.0008458515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00416494,"about_ca_topic_score_gemma":0.005500971,"domain_scores_codex":[0.9945271,0.001637748,0.0009157604,0.001404072,0.0005211364,0.0009941616],"domain_scores_gemma":[0.9965475,0.0005073643,0.0008467589,0.000875337,0.0007989019,0.0004241169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001915329,0.0004399244,0.001506769,0.0003692119,0.00006559223,0.000003874497,0.03195231,0.0009368191,0.386964,0.0135583,0.0000652246,0.5639464],"study_design_scores_gemma":[0.001193855,0.0002604124,0.02186492,0.003677139,0.0003813749,0.0001119736,0.001216725,0.08888908,0.5677963,0.3109739,0.002274028,0.001360353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3301063,0.002230981,0.6630589,0.0001719803,0.0004276282,0.001103442,0.00002027784,0.00002588023,0.002854583],"genre_scores_gemma":[0.4339266,0.001074545,0.5622745,0.00004964323,0.0002167075,0.0002701905,0.0003398327,0.00006426271,0.001783728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5625861,"threshold_uncertainty_score":0.9994594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106711572587451,"score_gpt":0.3530840163582807,"score_spread":0.2424128590995356,"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."}}