{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03656944,0.001986109,0.002289999,0.002559028,0.001445207,0.003769711,0.00457554,0.003692599,0.009433749],"category_scores_gemma":[0.08916776,0.001395905,0.004419541,0.002575239,0.008634119,0.007940439,0.005482222,0.008214774,0.002082175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003335967,"about_ca_system_score_gemma":0.003705295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002558076,"about_ca_topic_score_gemma":0.001708274,"domain_scores_codex":[0.9816451,0.01023901,0.0009856096,0.002865987,0.00358167,0.0006826422],"domain_scores_gemma":[0.91852,0.06343821,0.002683868,0.01082943,0.004192368,0.0003361569],"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":[0.00002778524,0.00003502729,0.0006386742,0.0002370071,0.00009357499,0.0001589662,0.0002480246,0.01606155,0.000533385,0.9591273,0.001466534,0.0213722],"study_design_scores_gemma":[0.00001520008,0.00006059565,0.0005654126,0.0001206079,0.00004595765,0.0000945396,0.00005408405,0.0657324,0.001237602,0.9259088,0.006129363,0.0000354299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001256401,0.0003718823,0.9938462,0.0007814491,0.00008163997,0.0000546869,0.00008184874,0.00009167665,0.003434216],"genre_scores_gemma":[0.1758925,0.002970709,0.8011099,0.003053885,0.001611681,0.001007377,0.0005517061,0.0005840207,0.01321822],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03656944,"threshold_uncertainty_score":0.1934,"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."}}