{"id":"W3215935162","doi":"10.1155/2021/5561191","title":"Estimation for Weibull Parameters with Generalized Progressive Hybrid Censored Data","year":2021,"lang":"en","type":"article","venue":"Journal of Mathematics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Yunnan Normal University; National Natural Science Foundation of China","keywords":"Censoring (clinical trials); Mathematics; Weibull distribution; Bayesian probability; Bayes' theorem; Statistics; Bayes factor; Monte Carlo method; Algorithm; Applied mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004831028,0.000999916,0.00092204,0.001417932,0.0003702528,0.001012883,0.002011078,0.001059684,0.001293503],"category_scores_gemma":[0.01758629,0.0004556064,0.001165911,0.001440018,0.001126802,0.002907419,0.001602945,0.001572092,0.0002800644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004879153,"about_ca_system_score_gemma":0.0007064571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001836314,"about_ca_topic_score_gemma":0.001230658,"domain_scores_codex":[0.9979689,0.0007853697,0.0001103391,0.0004631217,0.0005752017,0.0000971479],"domain_scores_gemma":[0.9925013,0.005304912,0.0005737288,0.001089778,0.0004557292,0.00007460122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002812168,0.0001219168,0.0122412,0.0006060142,0.0002789459,0.0003956711,0.0003881602,0.6646836,0.01080168,0.1033952,0.0007684447,0.2060378],"study_design_scores_gemma":[0.00001926674,0.00008858858,0.002102858,0.00003333951,0.00004625018,0.0001514036,0.00006126815,0.9431943,0.003863231,0.04923873,0.00115189,0.00004890348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0172093,0.0002048379,0.981892,0.00004380283,0.000008359032,0.00004403945,0.00007179894,0.000105473,0.0004203586],"genre_scores_gemma":[0.5838681,0.001066274,0.4122467,0.000107686,0.00007795378,0.0003409525,0.0007669306,0.00007396808,0.001451369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004831028,"threshold_uncertainty_score":0.02554923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1267988410930642,"score_gpt":0.3977147967626943,"score_spread":0.2709159556696301,"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."}}