{"id":"W2024201031","doi":"10.1080/09720510.2009.10701391","title":"Predictive densities from the Rayleigh Life Model under Type II censored samples","year":2009,"lang":"en","type":"article","venue":"Journal of Statistics and Management Systems","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rayleigh distribution; Predictive inference; Inference; Computer science; Bayesian inference; Statistics; Bayesian probability; Hyperparameter; Statistical inference; Hazard; Sample (material); Econometrics; Mathematics; Data mining; Frequentist inference; Machine learning; Probability density function; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002199527,0.0001028763,0.0002099675,0.0000362022,0.0002163829,0.00008937209,0.0001130301,0.00003066735,0.00002647375],"category_scores_gemma":[0.0002943698,0.00006907977,0.00002645234,0.00007527934,0.00005652809,0.00005741539,0.00002925184,0.00009025476,0.000002935202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003533454,"about_ca_system_score_gemma":0.00002689099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001200252,"about_ca_topic_score_gemma":0.000001877431,"domain_scores_codex":[0.998983,0.00004626256,0.0004673267,0.00009275608,0.000298654,0.0001120026],"domain_scores_gemma":[0.9985778,0.0005797859,0.0003365475,0.0001328708,0.000277895,0.00009514332],"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":[0.00002797617,0.00005705227,0.00001240635,0.00002854978,0.0001040263,0.00000334571,0.0001857423,0.001853856,0.00001519312,0.9029838,0.09437675,0.0003513097],"study_design_scores_gemma":[0.0007123674,0.0001699051,0.01683598,0.0001436367,0.0003969069,0.00001088897,0.002254048,0.2215807,0.000003878674,0.7544813,0.003262036,0.0001484284],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004024068,0.0001794783,0.9927259,0.001087396,0.0001291421,0.0002368131,0.0008945341,0.00001485193,0.0007078011],"genre_scores_gemma":[0.935948,0.0003170221,0.06279788,0.0004086688,0.0001061665,0.000005371504,0.00004764256,0.000008344963,0.000360856],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.931924,"threshold_uncertainty_score":0.2816991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07136402860229443,"score_gpt":0.3193674471612161,"score_spread":0.2480034185589216,"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."}}