{"id":"W6912358849","doi":"10.5281/zenodo.16551328","title":"Vraisemblance pour phénotypes via résistance génétique","year":2025,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Set (abstract data type); Identification (biology); Bayesian probability; Term (time)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00295793,0.001777499,0.002413913,0.001496378,0.001688337,0.00319764,0.0009528243,0.004212236,0.008320809],"category_scores_gemma":[0.008034389,0.0007730599,0.002346187,0.0007435126,0.001912171,0.002811311,0.001301454,0.007398075,0.002156251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001850248,"about_ca_system_score_gemma":0.0007220426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003118853,"about_ca_topic_score_gemma":0.00140497,"domain_scores_codex":[0.9961362,0.00152442,0.0001311069,0.000814954,0.001112677,0.0002806291],"domain_scores_gemma":[0.9945892,0.003849035,0.0002310456,0.0006879925,0.0003749511,0.0002678421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0019517,0.0006313808,0.004166498,0.0004256319,0.000219985,0.002942342,0.001047132,0.0327207,0.7082236,0.1531713,0.00408077,0.09041893],"study_design_scores_gemma":[0.001335904,0.002834756,0.03226094,0.0002160909,0.0003666417,0.009660694,0.0008809068,0.2384783,0.5028981,0.1190202,0.09154636,0.0005010834],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.501155,0.001715009,0.4567978,0.003508535,0.001115113,0.0001861025,0.0007208533,0.005896302,0.02890539],"genre_scores_gemma":[0.8493501,0.001871635,0.1074431,0.0007009443,0.0004932042,0.0001784847,0.0009937836,0.001355017,0.03761377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008320809,"threshold_uncertainty_score":0.02783591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03252195149327524,"score_gpt":0.2564495603493556,"score_spread":0.2239276088560804,"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."}}