{"id":"W2415011605","doi":"10.5539/ijsp.v5n4p1","title":"On the Estimation of Reliability of Weighted Weibull Distribution: A Comparative Study","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Estimator; Kernel density estimation; Statistics; Weibull distribution; Bayes estimator; Prior probability; Markov chain Monte Carlo; Nonparametric statistics; Bayesian probability; Applied mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001010258,0.00009827354,0.000266412,0.0000360771,0.00004951383,0.0000150702,0.0002161371,0.00003080292,0.0002267148],"category_scores_gemma":[0.004496145,0.00005245299,0.00005109644,0.00009892709,0.0003423906,0.00007260789,0.0000394179,0.00009706433,0.000002182157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008360303,"about_ca_system_score_gemma":0.00007630486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009056535,"about_ca_topic_score_gemma":0.000004111975,"domain_scores_codex":[0.9981641,0.0001942731,0.0009098457,0.0001206941,0.0005396552,0.00007146831],"domain_scores_gemma":[0.9922343,0.004918158,0.0008100686,0.0001829977,0.001800017,0.00005443964],"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":[0.0002173214,0.001012678,0.001511043,0.00002686737,0.00007802234,0.000001025034,0.0002637176,0.00003292662,0.00006130979,0.9907612,0.001951217,0.004082643],"study_design_scores_gemma":[0.0007595421,0.0003986667,0.04992549,0.00008726098,0.00005319577,0.00000647663,0.0001143181,0.003056472,0.0006940051,0.9447516,0.00009203696,0.00006097995],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4046764,0.000003488367,0.5915133,0.001109405,0.00006465575,0.0002454283,0.002288993,0.000003599574,0.00009467001],"genre_scores_gemma":[0.9795145,0.000006833664,0.02040297,0.00001174698,0.00001661941,0.00001175925,0.00002026823,0.000003203533,0.00001205628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5748382,"threshold_uncertainty_score":0.538263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05864850959386644,"score_gpt":0.3724729339987013,"score_spread":0.3138244244048349,"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."}}