{"id":"W1969152089","doi":"10.2143/ast.39.2.2044645","title":"On Parameter Estimation in Hierarchical Credibility","year":2009,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Credibility; Notation; Hierarchical database model; Variance (accounting); Computer science; Estimation; Function (biology); Variance components; Statistics; Econometrics; Applied mathematics; Mathematics; Algorithm; Data mining; Engineering; Arithmetic","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003877376,0.0001266838,0.0002410507,0.0001706464,0.00007953341,0.0001074714,0.000458331,0.0001112232,0.001145169],"category_scores_gemma":[0.0135045,0.00008923528,0.00008742496,0.0003788243,0.0001136995,0.00006776736,0.00005135588,0.0003323569,0.001688199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004686633,"about_ca_system_score_gemma":0.00003546975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002573417,"about_ca_topic_score_gemma":0.00001276092,"domain_scores_codex":[0.9972394,0.0004223436,0.0006161894,0.0005941769,0.0008538477,0.0002740582],"domain_scores_gemma":[0.9956015,0.003489948,0.00009110803,0.0006404032,0.00007391488,0.000103118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006567929,0.001029965,0.01069184,0.000005281861,0.000004246629,0.00003279378,0.0007996259,0.06237275,0.0001071411,0.05999172,0.05014247,0.8141654],"study_design_scores_gemma":[0.0003342199,0.0002282184,0.1291081,0.00001977681,0.000001564116,0.00000309706,0.00001049254,0.05479673,0.000101991,0.810627,0.004648045,0.0001206475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116423,0.00002415448,0.05159386,0.03020159,0.0001385922,0.0002581059,0.00000552755,0.00005557989,0.006080352],"genre_scores_gemma":[0.9802964,0.000001846036,0.01780196,0.001422426,0.00002656378,0.000007532908,0.000002531588,0.000003592422,0.000437088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8140447,"threshold_uncertainty_score":0.9997679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07989385119753083,"score_gpt":0.3691394118485626,"score_spread":0.2892455606510318,"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."}}