{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001547663,0.0001608884,0.000348285,0.0004973884,0.0001738727,0.0001320763,0.0003267071,0.0001047366,0.0002182259],"category_scores_gemma":[0.02399146,0.0001452071,0.00007067343,0.0003076223,0.000171319,0.0001121084,0.00001082568,0.0002733476,0.000006226783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00025289,"about_ca_system_score_gemma":0.003841409,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004615,"about_ca_topic_score_gemma":0.02041551,"domain_scores_codex":[0.9981576,0.00005440589,0.0007415264,0.0001230474,0.0002246445,0.0006987799],"domain_scores_gemma":[0.9931233,0.003904975,0.0004000678,0.0001773327,0.001069242,0.001325113],"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":[0.00001001652,0.00001713177,0.001069019,0.00005962944,0.00002865952,0.0001002113,0.0001522444,5.715542e-7,0.0000129689,0.8615789,0.01036821,0.1266025],"study_design_scores_gemma":[0.0004063642,0.0002558398,0.003012798,0.0000649021,0.00004779544,0.0000876311,0.0001345184,0.0002027743,0.0000598002,0.9853143,0.0102328,0.0001804386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004686354,0.00006036786,0.9958805,0.0006238118,0.0003988628,0.0001396055,0.0008457383,0.000007190516,0.001575309],"genre_scores_gemma":[0.1017562,0.00001117225,0.8974155,0.000256666,0.000460742,0.000001922931,0.000005576034,0.00002679641,0.00006540297],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.126422,"threshold_uncertainty_score":0.9974594,"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."}}