{"id":"W2029272463","doi":"10.1016/j.ejor.2011.01.016","title":"Globally optimal clusterwise regression by mixed logical-quadratic programming","year":2011,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Regression analysis; Integer programming; Quadratic equation; Mathematical optimization; Operations research; Mathematics; Statistics","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":[0.008105674,0.0001857254,0.0002365473,0.0003298608,0.0004753215,0.0006268449,0.002752191,0.00004086224,0.0001099451],"category_scores_gemma":[0.001154837,0.0001348032,0.0001075085,0.0007343857,0.0002621706,0.001253401,0.001192724,0.001031262,0.0002091403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855532,"about_ca_system_score_gemma":0.000372467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004419346,"about_ca_topic_score_gemma":6.236648e-7,"domain_scores_codex":[0.9932106,0.00233748,0.0007249838,0.0004026066,0.002646721,0.0006776629],"domain_scores_gemma":[0.9967219,0.0002947292,0.0001947655,0.0004673364,0.001848382,0.0004728714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001128827,0.002535498,0.0005501757,0.0001069273,0.0002547248,0.01021427,0.006655483,0.004740336,0.04227064,0.02800859,0.04898528,0.8545492],"study_design_scores_gemma":[0.0196739,0.04062528,0.03416206,0.003454725,0.00006058218,0.01237768,0.004874613,0.3937828,0.0634524,0.00699973,0.416163,0.004373269],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03445239,0.0005968409,0.9537784,0.001791332,0.0003097617,0.0003270373,0.000004067813,0.00005066948,0.008689516],"genre_scores_gemma":[0.5267134,0.00007667574,0.4715045,0.00009538291,0.0002520526,0.00000621497,0.00000323202,0.0000303232,0.001318201],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.850176,"threshold_uncertainty_score":0.604468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1333836776367603,"score_gpt":0.376373881362056,"score_spread":0.2429902037252957,"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."}}