{"id":"W3174543430","doi":"10.48550/arxiv.2107.00109","title":"Adaptive Capped Least Squares","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimator; Mathematical optimization; Least-squares function approximation; Stationary point; Mathematics; Quadratic equation; Quadratic programming; Algorithm; Integer (computer science); Optimization problem; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002270677,0.001640833,0.001604208,0.001082431,0.0005059002,0.001625447,0.003029469,0.002352865,0.003316611],"category_scores_gemma":[0.01214241,0.000862727,0.001227414,0.001520792,0.001526567,0.002167925,0.001697366,0.002826208,0.002394202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006102453,"about_ca_system_score_gemma":0.001056669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001934197,"about_ca_topic_score_gemma":0.001257895,"domain_scores_codex":[0.9967526,0.001131774,0.0001078896,0.0009641799,0.0009030703,0.0001405316],"domain_scores_gemma":[0.9959526,0.001886801,0.0003718846,0.0007853378,0.0009176522,0.00008580784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002884167,0.0001086338,0.00125235,0.0004724613,0.0002384909,0.0002258508,0.0001232034,0.5710776,0.02060214,0.04134535,0.01035034,0.3539152],"study_design_scores_gemma":[0.00001087256,0.00004873535,0.0001817749,0.00002000972,0.00001596916,0.00008940053,0.000008254437,0.9865115,0.003400448,0.005724453,0.003962196,0.00002641732],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001325932,0.0001977515,0.9973838,0.00007143644,0.00005339654,0.00001718536,0.00004243576,0.0002867753,0.0006212582],"genre_scores_gemma":[0.1535001,0.0008944139,0.8352537,0.0006041555,0.0003977053,0.0002385744,0.0007351391,0.0005012286,0.007875054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003316611,"threshold_uncertainty_score":0.01200867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3255809411667598,"score_gpt":0.3019174681366303,"score_spread":0.02366347303012956,"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."}}