{"id":"W2081612095","doi":"10.1002/cjs.11164","title":"A matching prior based on the modified profile likelihood in a generalized Weibull stress‐strength model","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Frequentist inference; Prior probability; Weibull distribution; Matching (statistics); Mathematics; Bayesian probability; Statistics; Bayesian inference; Statistical inference; Econometrics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004726518,0.00014041,0.0002128143,0.0001566852,0.0001728401,0.00006173279,0.0002086015,0.00006057432,0.0004482528],"category_scores_gemma":[0.001155677,0.0001063742,0.00004645961,0.0002074543,0.000074745,0.00007155528,0.000006787407,0.0003173514,0.00002136851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224921,"about_ca_system_score_gemma":0.0008250667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003779282,"about_ca_topic_score_gemma":0.002012471,"domain_scores_codex":[0.9986283,0.0000988234,0.0005324967,0.0000870783,0.0002594029,0.0003939603],"domain_scores_gemma":[0.9978993,0.0008787829,0.0002796046,0.0001988247,0.0002008745,0.0005426204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001244094,0.0001084706,0.0001448682,0.00002894527,0.00001120138,0.00001016573,0.0004437664,0.003189652,0.00001902605,0.9840246,0.01093503,0.001071875],"study_design_scores_gemma":[0.0009242611,0.00004694195,0.003502639,0.0001543351,0.00006530715,0.00001302293,0.0003294842,0.6373659,0.000100217,0.3569546,0.0003262443,0.0002170986],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02982217,0.00002289362,0.9636219,0.001172233,0.00008416959,0.0002575502,0.00387875,0.00000847643,0.001131925],"genre_scores_gemma":[0.8465893,0.000002255103,0.1528203,0.000379104,0.00005356936,0.00002299259,0.00005328018,0.00001826198,0.00006096595],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8167672,"threshold_uncertainty_score":0.4908054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06516023555310116,"score_gpt":0.30770965423153,"score_spread":0.2425494186784288,"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."}}