{"id":"W2050405957","doi":"10.1016/j.csda.2012.05.019","title":"Learned-loss boosting","year":2012,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Robustification; Boosting (machine learning); Covariate; Spline (mechanical); Regression; Mathematics; Statistics; Regression analysis; Mathematical optimization; Computer science; Econometrics; Artificial intelligence; Machine learning; Engineering","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":[0.003386285,0.001721158,0.002504632,0.001159978,0.0009027498,0.002212542,0.003321471,0.003301084,0.02068557],"category_scores_gemma":[0.008482973,0.0007826586,0.001253148,0.0011449,0.001005577,0.002309919,0.002842307,0.004132863,0.01437347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009367678,"about_ca_system_score_gemma":0.002167983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001189945,"about_ca_topic_score_gemma":0.002812511,"domain_scores_codex":[0.9976538,0.0007741927,0.00009235485,0.0006554054,0.0005890969,0.000235153],"domain_scores_gemma":[0.9976189,0.0006795974,0.00007978034,0.0009325208,0.0005298747,0.0001593111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004375765,0.0003157424,0.001093204,0.0002206584,0.0002307993,0.0001038949,0.00005394102,0.116807,0.004271779,0.04646338,0.09455413,0.7354479],"study_design_scores_gemma":[0.00007887937,0.0001072713,0.0003443549,0.00004947772,0.00007474711,0.0001773955,0.00001564884,0.9139686,0.004993974,0.05639829,0.02377127,0.00002000566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00634208,0.001337835,0.9713471,0.001039011,0.0006659767,0.0001710251,0.0005172926,0.006812555,0.01176717],"genre_scores_gemma":[0.2463176,0.001014632,0.6845812,0.002414608,0.001153669,0.0004712857,0.003612594,0.002176367,0.05825822],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02068557,"threshold_uncertainty_score":0.06920016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3353865682554928,"score_gpt":0.5084236974997517,"score_spread":0.1730371292442589,"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."}}