{"id":"W3159342866","doi":"10.6000/1929-6029.2021.10.02","title":"A Generalized Log-Weibull Distribution with Bio-Medical Applications","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Log-logistic distribution; Mathematics; Log-Cauchy distribution; Quantile function; Order statistic; Quantile; Exponentiated Weibull distribution; Statistics; Cumulative distribution function; Distribution (mathematics); Hazard; Applied mathematics; Distribution fitting; Probability density function; Inverse-chi-squared distribution; Probability distribution; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002019992,0.0008517814,0.0007979232,0.002083038,0.0006093136,0.001742086,0.001258579,0.001819404,0.004519861],"category_scores_gemma":[0.008230585,0.0003123644,0.001043867,0.003277715,0.001538129,0.001970035,0.001089568,0.001773947,0.001711909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007837206,"about_ca_system_score_gemma":0.0009411917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001516382,"about_ca_topic_score_gemma":0.000936666,"domain_scores_codex":[0.9991192,0.0002870617,0.00004660242,0.0001727693,0.0003033778,0.0000710172],"domain_scores_gemma":[0.9972706,0.001419498,0.0003724593,0.0003059781,0.0005388382,0.00009257312],"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":[0.0001264462,0.00007517223,0.006118149,0.0005303486,0.0001233405,0.002121165,0.000414694,0.1727077,0.006375813,0.6684327,0.01521329,0.1277612],"study_design_scores_gemma":[0.00002346127,0.0001034118,0.003753082,0.0001736605,0.00004996115,0.002929378,0.0002265376,0.3621763,0.001411425,0.5886151,0.04043515,0.0001025893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01242715,0.003880027,0.9727926,0.001608397,0.0003316188,0.00005267747,0.0004811753,0.0005238588,0.007902644],"genre_scores_gemma":[0.6416506,0.01172017,0.3164074,0.001653783,0.00141725,0.0004578088,0.001210129,0.0003533089,0.02512963],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004519861,"threshold_uncertainty_score":0.01512039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100467294601025,"score_gpt":0.5034979798115773,"score_spread":0.3934512503514748,"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."}}