{"id":"W2096179290","doi":"10.1920/wp.cem.2016.3616","title":"Valid post-selection and post-regularization inference: An elementary, general approach","year":2017,"lang":"en","type":"report","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"Economic and Social Research Council","keywords":"Inference; Regularization (linguistics); Selection (genetic algorithm); Econometrics; Computer science; Statistical inference; Mathematical economics; Mathematics; Artificial intelligence; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001277241,0.0004157761,0.0006442834,0.0001632452,0.0003197589,0.0003327211,0.0002742048,0.0004841697,0.0006648421],"category_scores_gemma":[0.004086237,0.0003388979,0.00008145863,0.00006843443,0.0001001672,0.0002287419,0.0001464049,0.0004181251,0.000005293392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319351,"about_ca_system_score_gemma":0.0008047545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002057172,"about_ca_topic_score_gemma":0.0001533274,"domain_scores_codex":[0.9973846,0.000245849,0.0006169893,0.0006560321,0.0007500644,0.0003464796],"domain_scores_gemma":[0.9970263,0.0002923082,0.0005372812,0.0006036393,0.001346692,0.0001938318],"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":[0.00008240368,0.0007924381,0.003962207,0.002283798,0.0004668644,0.00001259362,0.0004507172,0.000003922438,0.004400222,0.6095479,0.01167231,0.3663247],"study_design_scores_gemma":[0.001431528,0.002752005,0.03900654,0.0004256924,0.001621449,0.0004425459,0.0003347946,0.03107448,0.001950498,0.901426,0.01649242,0.003042005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01825685,0.0000947708,0.8108106,0.0001123911,0.0007834247,0.001139964,0.0004079899,0.0002284128,0.1681655],"genre_scores_gemma":[0.01151251,0.0003236235,0.9607674,0.0001312772,0.0008763192,0.00008897045,0.001550658,0.00009592355,0.02465328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3632827,"threshold_uncertainty_score":0.9999063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1568402781928854,"score_gpt":0.4386386995195704,"score_spread":0.281798421326685,"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."}}