{"id":"W3125130726","doi":"","title":"Predictive Quantile Regression with Mixed Roots and Increasing Dimensions","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lasso (programming language); Quantile regression; Quantile; Econometrics; Oracle; Statistics; Regression; Mathematics; Sample (material); Cross-sectional regression; Regression analysis; Variable (mathematics); Feature selection; Computer science; Artificial intelligence; Polynomial regression; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003012385,0.0002763312,0.0004540801,0.0000985342,0.0001686966,0.00005940887,0.0001781607,0.0002406207,0.00006753296],"category_scores_gemma":[0.0009973508,0.000233585,0.00006645217,0.0002105726,0.0001916068,0.00008409446,0.0007672998,0.0005309386,0.000001810811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007173324,"about_ca_system_score_gemma":0.0001381424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001809275,"about_ca_topic_score_gemma":0.00008154433,"domain_scores_codex":[0.9983351,0.0004504527,0.0001696494,0.0007195969,0.0000939667,0.0002312626],"domain_scores_gemma":[0.9973314,0.001529708,0.0002167007,0.0005328005,0.0002047255,0.0001846523],"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":[0.001246022,0.0006824937,0.04352957,0.001249804,0.0007269692,0.002558921,0.001247285,0.003447979,0.00116295,0.9412606,0.0005748659,0.002312476],"study_design_scores_gemma":[0.001427776,0.000383077,0.04458227,0.004556483,0.001115769,0.00006492571,0.002583455,0.2689959,0.0007993909,0.6743328,0.00004262863,0.001115475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6820432,0.00004908915,0.3165645,0.00001286035,0.0001053556,0.0001715938,0.0000358586,0.0000645212,0.0009530762],"genre_scores_gemma":[0.9218527,0.0001236463,0.07775485,0.00001399192,0.00002648294,0.00000103297,0.0000144117,0.00002460811,0.0001882563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2669278,"threshold_uncertainty_score":0.9525321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306418424052272,"score_gpt":0.2638571199174573,"score_spread":0.1332152775122301,"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."}}