{"id":"W4200571536","doi":"10.5539/ijsp.v11n1p40","title":"Parameters Estimation for Wear-out Failure Period of Three-Parameter Weibull Distribution","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperparameter; Estimator; Shape parameter; Mathematics; Weibull distribution; Statistics; Monte Carlo method; Sample size determination; Scale parameter; Hyperparameter optimization; Estimation theory; Linear regression; Applied mathematics; Algorithm; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003030699,0.0006860453,0.000656609,0.001651367,0.0004545152,0.0006334918,0.0009059422,0.0008558469,0.001067083],"category_scores_gemma":[0.0102818,0.0003045761,0.0009962522,0.001080006,0.0003943908,0.001106464,0.000646895,0.001120935,0.0002919805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006001501,"about_ca_system_score_gemma":0.0006147672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00390648,"about_ca_topic_score_gemma":0.002675151,"domain_scores_codex":[0.9992048,0.0002477077,0.0000612291,0.0001952218,0.0002048607,0.0000862001],"domain_scores_gemma":[0.9959045,0.002297119,0.0004982633,0.000461089,0.0007572948,0.00008176811],"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":[0.0002992971,0.0001011486,0.03470527,0.0003774965,0.0001543487,0.0003235791,0.0004535717,0.7675412,0.01453077,0.006738772,0.002057682,0.1727169],"study_design_scores_gemma":[0.00001383974,0.00004985821,0.008421984,0.00002653456,0.00002996528,0.0001533173,0.00007969752,0.9807116,0.006100554,0.003675168,0.0006894895,0.00004799539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1311122,0.0006420864,0.866425,0.0001003909,0.00002393114,0.00006678593,0.0001883526,0.0005949438,0.000846352],"genre_scores_gemma":[0.8734443,0.0003435221,0.1244296,0.00004310498,0.0000209839,0.0001560216,0.0005018478,0.000144125,0.0009163866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00390648,"threshold_uncertainty_score":0.01602811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02198783875791575,"score_gpt":0.2641721221547378,"score_spread":0.242184283396822,"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."}}