{"id":"W2156727934","doi":"10.1142/9781860947995_0011","title":"SELECTING GENES WITH DISSIMILAR DISCRIMINATION STRENGTH FOR SAMPLE CLASS PREDICTION","year":2007,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Class (philosophy); Sample (material); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001725047,0.0000887273,0.00005598814,0.00004016317,0.0001174645,0.00001864567,0.00006200328,0.00007875999,0.00001225551],"category_scores_gemma":[0.00005132036,0.00006880481,0.00003231127,0.00008180256,0.00001872402,0.000004980981,0.00001409971,0.00003095166,4.539547e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001787108,"about_ca_system_score_gemma":0.00003533734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007855928,"about_ca_topic_score_gemma":0.0001574012,"domain_scores_codex":[0.9993431,0.00001170468,0.0001338894,0.0002513821,0.00009722681,0.0001626852],"domain_scores_gemma":[0.9995971,0.0000200103,0.00006448275,0.0001550026,0.0001095563,0.00005378741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002629974,0.00006701324,0.009313574,0.00002374287,0.00002633546,1.110763e-7,0.00006365099,0.0001223337,0.9292173,0.0007686251,0.003359597,0.05677475],"study_design_scores_gemma":[0.0005376571,0.0003295348,0.008965907,0.000009589405,0.00001955658,0.000002708755,0.0006566026,0.0009703916,0.8688355,0.00007285719,0.1194734,0.0001263333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2739556,0.00009976745,0.7236241,0.000188814,0.0001129412,0.000247408,0.00002734341,0.00003094328,0.00171313],"genre_scores_gemma":[0.9885535,0.0000389313,0.009634783,0.0001016786,0.0002739403,0.00004806945,0.0004756547,0.00001654763,0.0008568761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7145979,"threshold_uncertainty_score":0.2805779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582956701452087,"score_gpt":0.276695134233653,"score_spread":0.2608655672191321,"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."}}