{"id":"W4416967053","doi":"10.1080/10618600.2025.2596057","title":"Robust Multi-Model Subset Selection","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Selection (genetic algorithm); Robustness (evolution); Robust statistics; Cheminformatics; Estimator; Code (set theory); Source code","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003864939,0.000115459,0.0002849979,0.0001545694,0.0001229556,0.0000320049,0.00007182425,0.00006595838,0.00001026955],"category_scores_gemma":[0.0009755344,0.00009259414,0.00005548616,0.0002039321,0.0001092417,0.00007787736,0.00002436419,0.0002782683,3.231091e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002292684,"about_ca_system_score_gemma":0.00009403792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000120058,"about_ca_topic_score_gemma":0.000004455679,"domain_scores_codex":[0.9988589,0.00008253552,0.0005419558,0.000118831,0.000261918,0.0001358233],"domain_scores_gemma":[0.9971619,0.001914538,0.0002268602,0.00003895513,0.0005482487,0.0001094797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006932479,0.00014947,0.0003064928,0.00006693211,0.0000606727,0.00001159999,0.00003438939,0.05893367,0.00003085277,0.9278286,0.002852846,0.00965514],"study_design_scores_gemma":[0.0004243635,0.00005789727,0.002205069,0.00002839464,0.0000476403,0.00003047212,0.00000700526,0.4075264,0.000004255098,0.5895208,0.00009260292,0.00005516802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005203462,0.00007654262,0.9940174,0.0003477452,0.00009902565,0.00006768404,0.0001129046,0.00001045018,0.00006477238],"genre_scores_gemma":[0.08230458,0.00004206966,0.9172201,0.000244417,0.00003108962,0.000001508164,0.000005246795,0.000007633331,0.0001433385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3485927,"threshold_uncertainty_score":0.3775879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103888632090066,"score_gpt":0.4074373059381189,"score_spread":0.3035486738480529,"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."}}