{"id":"W2051474485","doi":"10.1198/004017004000000482","title":"Optimal Progressive Censoring Plans for the Weibull Distribution","year":2004,"lang":"en","type":"article","venue":"Technometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Censoring (clinical trials); Weibull distribution; Statistics; Mathematics; Fisher information; Maximum likelihood; Econometrics","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":[0.0002105692,0.0001109541,0.0001177742,0.00009196333,0.000341513,0.00005385247,0.0002343328,0.0000779512,0.00004008807],"category_scores_gemma":[0.003007218,0.00008198478,0.00006563772,0.001317833,0.0001069102,0.00004970003,0.00003978337,0.0001300025,0.00003688229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659827,"about_ca_system_score_gemma":0.0000338166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002581164,"about_ca_topic_score_gemma":7.08365e-7,"domain_scores_codex":[0.9991279,0.000005000256,0.0002476454,0.0001747136,0.000214772,0.0002299518],"domain_scores_gemma":[0.998469,0.000892271,0.0001206943,0.0002848865,0.000173381,0.00005976733],"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":[0.000005752994,0.0001124659,0.00002006363,0.00002364557,0.00001222,6.614002e-7,0.00001759768,0.0002421227,0.00002084164,0.9916807,0.00212344,0.005740455],"study_design_scores_gemma":[0.002540913,0.0002310402,0.006034316,0.00008109199,0.0002791487,0.00004114498,0.0005629059,0.01965953,0.008139861,0.8848124,0.0769625,0.0006552048],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006118723,0.00008545387,0.9886557,0.001783681,0.00005486379,0.0007924644,0.002010412,0.0003185516,0.0001801684],"genre_scores_gemma":[0.905287,0.00001501171,0.09353757,0.00004330524,0.00006219571,0.0006096579,0.0003547352,0.0000172621,0.00007327719],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8991683,"threshold_uncertainty_score":0.3600138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08562049267018103,"score_gpt":0.3740222215509656,"score_spread":0.2884017288807846,"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."}}