{"id":"W6894035228","doi":"10.5281/zenodo.6450555","title":"Multi-Model Subset Selection","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Replicate; Selection (genetic algorithm); Expression (computer science); Feature selection; Scripting language; Set (abstract data type)","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":[0.002194079,0.001873114,0.001299809,0.001287621,0.0006104547,0.001229632,0.001916485,0.0008031651,0.1429464],"category_scores_gemma":[0.006825351,0.0009108046,0.001754448,0.001240853,0.000230563,0.000791985,0.0008282689,0.001212243,0.05016757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005518416,"about_ca_system_score_gemma":0.001294093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00361256,"about_ca_topic_score_gemma":0.005130934,"domain_scores_codex":[0.9994026,0.0002186698,0.00005060949,0.0001297818,0.0001422151,0.00005601794],"domain_scores_gemma":[0.9969536,0.00198911,0.0001032254,0.0004687107,0.0003973714,0.00008789705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009772024,0.0003346155,0.00527794,0.00128307,0.0006308523,0.0005006396,0.0002493034,0.07905816,0.005239659,0.01221991,0.76261,0.1316187],"study_design_scores_gemma":[0.001116969,0.0002952822,0.004249862,0.0001678025,0.0003023196,0.0004093821,0.0001182047,0.6115587,0.01929884,0.02564088,0.3366858,0.0001559601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01653151,0.0003973475,0.3899437,0.0004336213,0.0004276012,0.0007215475,0.2454962,0.3208974,0.02515108],"genre_scores_gemma":[0.09656074,0.0004420663,0.3511623,0.0005361341,0.0001761412,0.003473087,0.4285204,0.0931783,0.02595091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1429464,"threshold_uncertainty_score":0.4782034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05282680588023136,"score_gpt":0.2787573757112987,"score_spread":0.2259305698310674,"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."}}