{"id":"W4302009424","doi":"10.1016/j.spl.2022.109693","title":"Theoretical properties of Bayesian Student-<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" id=\"d1e420\" altimg=\"si2.svg\"><mml:mi>t</mml:mi></mml:math> linear regression","year":2022,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Bayesian probability; Linear regression; Regression; Robustness (evolution); Applied mathematics; Calculus (dental); Algorithm; Statistics; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001515488,0.0003911496,0.0004319549,0.00007274171,0.0005390148,0.00008639548,0.0005854545,0.0001904584,0.0001424994],"category_scores_gemma":[0.002750796,0.0003799531,0.0002234189,0.0001925358,0.001133802,0.0001608758,0.0007678664,0.000775456,0.00003658107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005782692,"about_ca_system_score_gemma":0.0001989427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005158804,"about_ca_topic_score_gemma":0.00002718585,"domain_scores_codex":[0.9958557,0.0005011485,0.001045288,0.000743431,0.001174275,0.000680142],"domain_scores_gemma":[0.9965408,0.001576889,0.0005492976,0.0009743929,0.0001200033,0.0002386557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003910613,0.0004784689,0.00001652538,0.001079311,0.0001331996,0.000117833,0.001740372,0.000334813,0.003082311,0.9902588,0.0004755686,0.001891699],"study_design_scores_gemma":[0.000530493,0.0005128002,0.00002239495,0.000237681,0.0002497461,0.00004287307,0.0002748264,0.5038424,0.002120804,0.4914504,0.0003346259,0.000380981],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5269715,0.00004221356,0.4711721,0.0005200556,0.0002607405,0.0001777779,0.0006439011,0.00007445661,0.0001373127],"genre_scores_gemma":[0.5002837,0.00001827948,0.4985557,0.0005271032,0.0001172377,0.0002796286,0.0001058816,0.00009342111,0.00001907403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5035076,"threshold_uncertainty_score":0.9998652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03953097163700605,"score_gpt":0.3183988694316784,"score_spread":0.2788678977946724,"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."}}