{"id":"W2908210970","doi":"10.5539/mas.v13n2p54","title":"The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Mathematics; Statistics; Exponentiated Weibull distribution; Estimator; Applied mathematics; Inverse; Rayleigh distribution; Fisher information; Log-logistic distribution; Exponential function; Exponential distribution; Distribution fitting; Probability density function; Mathematical analysis","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":[0.0005714145,0.0001247585,0.0001235198,0.00002397839,0.0007500771,0.0001882645,0.0003550365,0.00004172801,0.00003683878],"category_scores_gemma":[0.0001632002,0.00008617833,0.00002148756,0.0003675104,0.0007432596,0.000109086,0.000129429,0.0001042211,0.0002132868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009333174,"about_ca_system_score_gemma":0.00008513003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007191607,"about_ca_topic_score_gemma":0.000008078071,"domain_scores_codex":[0.9986428,0.00001909448,0.0002505654,0.0003821921,0.0004230679,0.0002822967],"domain_scores_gemma":[0.9990206,0.00015791,0.0001062023,0.0004626534,0.0001324028,0.0001202807],"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":[0.00001206124,0.00002843004,0.00003840412,0.000014948,0.000002698615,5.667852e-8,0.00009389654,0.0000207204,0.05427618,0.938391,0.0007277337,0.006393913],"study_design_scores_gemma":[0.0004255739,0.00001194339,0.002049475,0.000008960037,0.00001459333,0.000004970008,0.0001309634,0.5670173,0.00428395,0.4164013,0.009441874,0.000209069],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08322817,0.00003025733,0.9096608,0.001495604,0.00004580824,0.0008908256,0.00006534062,0.0001269545,0.004456277],"genre_scores_gemma":[0.9946913,0.00001269423,0.004281625,0.0001543249,0.00001927959,0.0002976079,0.00003353197,0.000008826037,0.0005008131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9114631,"threshold_uncertainty_score":0.5769063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05013221205306206,"score_gpt":0.2947071846523546,"score_spread":0.2445749725992925,"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."}}