{"id":"W2106891714","doi":"10.1002/cjs.5550350301","title":"Robust likelihood inference for public policy","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistic; Statistics; Jackknife resampling; Mathematics; Inference; Variance (accounting); Statistical inference; Econometrics; Likelihood-ratio test; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03157094,0.001684587,0.003813871,0.004989451,0.001144774,0.004599375,0.003936912,0.003661831,0.008754223],"category_scores_gemma":[0.2104178,0.001448673,0.00209814,0.005267972,0.00521944,0.004371859,0.003622165,0.006379313,0.001660898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005585421,"about_ca_system_score_gemma":0.003604834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008136625,"about_ca_topic_score_gemma":0.004080761,"domain_scores_codex":[0.9730405,0.02112051,0.0006685099,0.002069308,0.002498721,0.0006025268],"domain_scores_gemma":[0.8476492,0.1371695,0.005911796,0.005125327,0.003490382,0.0006538537],"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":[0.0000704938,0.00003764527,0.0007753162,0.0001980078,0.0002276747,0.0001110802,0.0001050873,0.1637363,0.0001058559,0.8008767,0.003695045,0.03006098],"study_design_scores_gemma":[0.00002885883,0.00001432255,0.0001720555,0.00003886952,0.00002209025,0.00001937282,0.00001873512,0.2866973,0.0001115791,0.7112885,0.001572812,0.00001553726],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002679662,0.0008473496,0.9904676,0.001281661,0.00008095455,0.0000525117,0.0001919106,0.0002641934,0.004134146],"genre_scores_gemma":[0.4669332,0.003019226,0.515946,0.0008225037,0.001002943,0.001055081,0.001203176,0.0005285889,0.009489151],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03157094,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165656890241688,"score_gpt":0.36354187455468,"score_spread":0.2469761855305113,"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."}}