{"id":"W2020813649","doi":"10.1002/qre.833","title":"Comparison of Weibull small samples using Monte Carlo simulations","year":2006,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"University of Toronto","keywords":"Sample size determination; Weibull distribution; Monte Carlo method; Type I and type II errors; Reliability (semiconductor); Microelectronics; Sample (material); Reliability engineering; Statistics; Computer science; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02239607,0.0008635774,0.00136301,0.003458875,0.0007694674,0.001651388,0.001482247,0.001554092,0.004278775],"category_scores_gemma":[0.09161864,0.0005956866,0.001161062,0.001931151,0.001295998,0.001878969,0.00100885,0.00162807,0.0004269826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001417053,"about_ca_system_score_gemma":0.0009131021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003487085,"about_ca_topic_score_gemma":0.002392001,"domain_scores_codex":[0.9922035,0.005835505,0.0002586194,0.0004363905,0.001025787,0.0002401518],"domain_scores_gemma":[0.7785423,0.2076946,0.00312351,0.00588879,0.004184101,0.0005666957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005768032,0.0001652,0.00367756,0.0001325365,0.0001540371,0.000120226,0.000131312,0.942992,0.0005193035,0.03328006,0.001038181,0.01721276],"study_design_scores_gemma":[0.00004365012,0.0001182355,0.0008934534,0.00003682678,0.00002141633,0.0000401061,0.00004416302,0.9842405,0.0007767929,0.0132898,0.0004711486,0.00002392758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3278544,0.001398834,0.661608,0.0005043056,0.0002069504,0.0005531543,0.0004026861,0.0007552309,0.006716422],"genre_scores_gemma":[0.9155465,0.0003617356,0.08189464,0.0001129059,0.00004843122,0.0004984283,0.0004824721,0.0001400921,0.0009148752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02239607,"threshold_uncertainty_score":0.1184432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1803562299929397,"score_gpt":0.4283226680784923,"score_spread":0.2479664380855525,"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."}}