{"id":"W2896398456","doi":"10.1080/01621459.2018.1543124","title":"Adaptive Huber Regression","year":2018,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":305,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Regression; Statistics; Mathematics; Econometrics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0009780569,0.0001081108,0.0003776027,0.00003730761,0.0001338232,0.00002056839,0.0001879758,0.00003545407,0.00006715975],"category_scores_gemma":[0.01120559,0.00006048599,0.00009954921,0.0002142548,0.0002246966,0.00009210534,0.0000561499,0.000289617,0.00001140495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003117971,"about_ca_system_score_gemma":0.00006123624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008752677,"about_ca_topic_score_gemma":0.000005207879,"domain_scores_codex":[0.9980925,0.000494252,0.000481169,0.0001058675,0.0006089123,0.0002172722],"domain_scores_gemma":[0.9939537,0.003525955,0.001685158,0.0001463896,0.0005893654,0.00009943263],"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":[0.0008135856,0.0003778247,0.003893908,0.00002093933,0.0003434654,0.00002438151,0.0007868166,0.00001238423,0.001700982,0.6331072,0.1430194,0.2158991],"study_design_scores_gemma":[0.0003178707,0.0006429921,0.0134524,0.00006539885,0.0001368811,0.00001611942,0.0001540633,0.001322006,0.0004406523,0.9808314,0.00250621,0.0001139291],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02183609,0.000008950914,0.9752681,0.000910964,0.0003739506,0.00007707992,0.00004992911,0.000010473,0.001464477],"genre_scores_gemma":[0.367176,0.000008304701,0.6314394,0.000323886,0.0004122458,0.000001359422,3.006683e-7,0.00001457914,0.0006239661],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3477242,"threshold_uncertainty_score":0.9971234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0743662802260562,"score_gpt":0.4399700873989351,"score_spread":0.3656038071728789,"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."}}