{"id":"W4397005191","doi":"10.1142/s0219477524500561","title":"Multi-Response Bridge Regularization Parameter Selection via Multivariate Generalized Information Criterion","year":2024,"lang":"en","type":"article","venue":"Fluctuation and Noise Letters","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multivariate statistics; Selection (genetic algorithm); Regularization (linguistics); Applied mathematics; Mathematics; Statistics; Computer science; Mathematical optimization; Artificial intelligence","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":[0.0002621693,0.0001456375,0.00009644093,0.0002286132,0.00008686708,0.0002689855,0.00003513621,0.0000821025,0.00003661685],"category_scores_gemma":[0.00008114328,0.0001454931,0.00003638367,0.0002244602,0.00001872232,0.0008714159,0.00000890309,0.0001181425,0.00005620353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009102637,"about_ca_system_score_gemma":0.00001051485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001969124,"about_ca_topic_score_gemma":0.000001621943,"domain_scores_codex":[0.9992437,0.0000637406,0.0002552985,0.0001459749,0.0001419126,0.0001493589],"domain_scores_gemma":[0.9997254,0.00007110409,0.00002962536,0.00007927432,0.00004631674,0.00004826969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005601524,0.0000061019,0.00002519059,0.00009376229,0.00002797739,0.000001095535,0.0009880469,0.02210876,0.9495919,0.00005995975,0.0008041729,0.026237],"study_design_scores_gemma":[0.0004152811,0.00001776781,0.0180499,0.00003954313,0.00003030409,0.0000124162,0.000006341101,0.9643735,0.01443518,0.00008281279,0.002357202,0.0001797413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4481322,0.00005027369,0.5505117,0.0005248998,0.000347323,0.0001573117,0.000008795601,0.0002622666,0.000005199157],"genre_scores_gemma":[0.9877577,0.00003985322,0.01115595,0.0006219385,0.00009755942,0.00003500555,0.0002268998,0.0000243854,0.00004065326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9422647,"threshold_uncertainty_score":0.5933034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009867978981762367,"score_gpt":0.2239361501199428,"score_spread":0.2140681711381804,"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."}}