{"id":"W4396860342","doi":"10.5802/ojmo.27","title":"Cardinality-constrained structured data-fitting problems","year":2024,"lang":"en","type":"article","venue":"Open Journal of Mathematical Optimization","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cardinality (data modeling); Dual (grammatical number); Iterated function; Mathematical optimization; Constraint (computer-aided design); Computer science; Simple (philosophy); Algorithm; Identification (biology); Mathematics; Data mining","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002165686,0.0001120192,0.0002743006,0.0000977713,0.00008901272,0.001459219,0.001914257,0.00004576888,0.0001599983],"category_scores_gemma":[0.0004358432,0.00007922773,0.00006159644,0.0003362461,0.00002986507,0.001611998,0.0005588655,0.000298717,0.00001432754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002403035,"about_ca_system_score_gemma":0.0001461004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002587326,"about_ca_topic_score_gemma":1.605782e-7,"domain_scores_codex":[0.9985564,0.0001448805,0.0005628307,0.0002279468,0.0003571996,0.000150734],"domain_scores_gemma":[0.9988898,0.0002156142,0.0002268366,0.0004290163,0.0001365486,0.0001021525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009816555,0.0001113607,0.00002518657,0.0004098311,0.0002236081,0.0001565221,0.001721244,0.7160177,0.0001203075,0.1303907,0.001987593,0.1488262],"study_design_scores_gemma":[0.0002018989,0.000063667,0.000005932672,0.0003586772,0.00002948952,0.0004970122,0.00003230489,0.9776734,0.00003429482,0.01989596,0.001110709,0.00009664403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001457812,0.0001725896,0.9926912,0.002205466,0.0003172075,0.0001459097,0.000003636825,0.00004439421,0.004273876],"genre_scores_gemma":[0.07246377,0.00001440022,0.9270137,0.00005576781,0.0001749201,0.000001574843,0.000008527138,0.00001312236,0.0002542376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2616557,"threshold_uncertainty_score":0.9995773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04185271483798456,"score_gpt":0.3248309299069888,"score_spread":0.2829782150690042,"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."}}