{"id":"W4302024010","doi":"10.1016/j.aej.2022.09.023","title":"Statistical inferences for the extended inverse Weibull distribution under progressive type-II censored sample with applications","year":2022,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"King Saud University","keywords":"Weibull distribution; Mathematics; Statistical inference; Inference; Reliability (semiconductor); Inverse; Moment (physics); Applied mathematics; Distribution (mathematics); Sample (material); Representation (politics); Statistics; Computer science; Artificial intelligence; Power (physics); Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002439108,0.0001335757,0.0001433708,0.00003509202,0.001151417,0.00008183857,0.0002001363,0.00002910461,0.0009328163],"category_scores_gemma":[0.0005883938,0.00009548881,0.00004297888,0.0002846115,0.00007630112,0.00006115474,0.00005546259,0.0003014474,0.000004701123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332091,"about_ca_system_score_gemma":0.0001033218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003548086,"about_ca_topic_score_gemma":0.000002591328,"domain_scores_codex":[0.9989866,0.00003475332,0.0002859966,0.000155988,0.0002946958,0.0002419477],"domain_scores_gemma":[0.9977553,0.001565313,0.0001505855,0.000182945,0.0002250713,0.0001207671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004799022,0.0001375365,0.00003420443,0.00001938265,0.00007025721,0.000001482001,0.00006507739,0.01066359,0.00003673452,0.976474,0.01119798,0.001251693],"study_design_scores_gemma":[0.003205506,0.0009047755,0.01179451,0.00005898599,0.0006401386,0.0006509012,0.001371785,0.4672854,0.0001303121,0.2905747,0.222584,0.0007989479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001474915,0.00004461091,0.9947883,0.0009331854,0.0001034046,0.0006342664,0.001918215,0.00007711061,0.00002596717],"genre_scores_gemma":[0.7872109,0.00001632861,0.2095429,0.00009738959,0.000195671,0.001702241,0.001075379,0.00003428181,0.0001248737],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.785736,"threshold_uncertainty_score":0.9999804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03823126424033476,"score_gpt":0.3206413750367699,"score_spread":0.2824101107964352,"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."}}