{"id":"W4205927369","doi":"10.1016/j.csda.2021.107419","title":"Distributed adaptive Huber regression","year":2022,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Regression; Regression analysis; Multivariate adaptive regression splines; Statistics; Econometrics; Computer science; Mathematics; Nonparametric regression","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006046893,0.0001465748,0.0004360668,0.0004014625,0.0003903932,0.00003941226,0.0004113612,0.00002884886,0.002372532],"category_scores_gemma":[0.0003495186,0.0001318267,0.00009050527,0.001811046,0.00008137789,0.00008062,0.0009278571,0.0002765885,0.00006891684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002945364,"about_ca_system_score_gemma":0.000306654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002241539,"about_ca_topic_score_gemma":0.00002819422,"domain_scores_codex":[0.9970461,0.0002563118,0.0004268364,0.0005553011,0.001468227,0.0002472468],"domain_scores_gemma":[0.9977966,0.0006161598,0.0001813104,0.0008609054,0.0003714543,0.0001735526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000695196,0.0007658666,0.2626105,0.0001816366,0.00844346,0.001150045,0.0005248892,0.1713528,0.00002616633,0.01536045,0.5336513,0.005237714],"study_design_scores_gemma":[0.0008658905,0.0001045554,0.03053558,0.0000220291,0.001861388,0.0005560203,0.0003291614,0.9521634,0.00000127955,0.003623992,0.009772009,0.0001646838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01154703,0.0002755488,0.949594,0.0003710845,0.00007399794,0.0002630441,0.03757158,0.00005154777,0.0002521359],"genre_scores_gemma":[0.7249914,0.00002079323,0.1050846,0.0001540739,0.00007702481,0.00004669252,0.1686251,0.00002554723,0.0009747307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8445094,"threshold_uncertainty_score":0.9985394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07218353233010939,"score_gpt":0.3690273349082471,"score_spread":0.2968438025781377,"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."}}