{"id":"W2523193242","doi":"","title":"The Chen-Tindall system and the lasso operator: improving automatic model performance","year":2016,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lasso (programming language); Chen; Autoregressive model; Elastic net regularization; Operator (biology); Bayesian probability; Econometrics; Computer science; Artificial intelligence; Algorithm; Data mining; Mathematics; Feature selection; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01331574,0.001802066,0.002086847,0.001323585,0.001129879,0.001976576,0.001796893,0.00214621,0.004744214],"category_scores_gemma":[0.03294924,0.0007679482,0.001291533,0.001160927,0.001050986,0.002190296,0.00291502,0.004266642,0.001696258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000654072,"about_ca_system_score_gemma":0.002407354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005822269,"about_ca_topic_score_gemma":0.006740218,"domain_scores_codex":[0.9905956,0.006326483,0.00042373,0.001042189,0.001281542,0.0003305302],"domain_scores_gemma":[0.9849682,0.01014876,0.000947835,0.001943204,0.001689493,0.0003025025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001507223,0.0003654249,0.005971426,0.0003966387,0.0008865626,0.0002277553,0.0004722809,0.4501522,0.007094352,0.02374563,0.02393846,0.4852421],"study_design_scores_gemma":[0.00003304197,0.00006006115,0.0004480325,0.00001280962,0.00001706753,0.00002354185,0.00001826102,0.9930243,0.0008346502,0.004637467,0.0008663508,0.00002435149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03656182,0.001159367,0.9532185,0.001371512,0.00021512,0.00009936187,0.0004317551,0.003458649,0.003483889],"genre_scores_gemma":[0.4644736,0.0004480186,0.5266462,0.00110804,0.0004406483,0.0002953262,0.002096448,0.0009175814,0.003574119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01331574,"threshold_uncertainty_score":0.07042122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05150151622212161,"score_gpt":0.2597158671599309,"score_spread":0.2082143509378093,"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."}}