{"id":"W2892740843","doi":"10.1111/sjos.12353","title":"Hard thresholding regression","year":2018,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Mental Health; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Connaught Fund","keywords":"Mathematics; Oracle; Thresholding; Estimator; Regression; Linear regression; Regularization (linguistics); Range (aeronautics); Regression analysis; Property (philosophy); Statistics; Algorithm; Applied mathematics; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008250552,0.0001497448,0.0003645744,0.0001179069,0.0001468017,0.00005967376,0.0002448505,0.00006382149,0.00087081],"category_scores_gemma":[0.003593163,0.000105332,0.00005997866,0.0001612558,0.0002540355,0.00008840443,0.00004228798,0.0002740959,0.00002673272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005837722,"about_ca_system_score_gemma":0.00006992239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002818361,"about_ca_topic_score_gemma":0.00000123017,"domain_scores_codex":[0.9984387,0.0001193773,0.0006323769,0.0001221669,0.0004240908,0.0002632959],"domain_scores_gemma":[0.9973969,0.0009954809,0.0005555194,0.0001908201,0.0006460819,0.0002151707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001744479,0.00009848177,0.005906171,0.00009922542,0.00006691522,0.0001973036,0.0007381019,3.261935e-7,0.001831096,0.7955856,0.09032345,0.1049789],"study_design_scores_gemma":[0.0005436573,0.0009292957,0.008796959,0.0006560133,0.00008760647,0.0002152129,0.0001730722,0.000335217,0.002038016,0.9844796,0.001568126,0.0001772177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03743301,0.00007072381,0.9581998,0.0001202835,0.0009568755,0.00006570765,0.0001113996,0.00001380044,0.003028374],"genre_scores_gemma":[0.2551942,0.00003399215,0.7438626,0.00006593569,0.0005011145,5.995977e-7,8.661806e-7,0.00001917395,0.0003214476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2177612,"threshold_uncertainty_score":0.9534759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139632685768277,"score_gpt":0.3995155291384213,"score_spread":0.2598828433701442,"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."}}