{"id":"W4226120522","doi":"10.28924/2291-8639-20-2022-23","title":"On Some Properties of a New Truncated Model With Applications to Lifetime Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Weibull distribution; Quantile function; Log-logistic distribution; Applied mathematics; Lorenz curve; Log-Cauchy distribution; Statistics; Distribution (mathematics); Beta distribution; Power function; Order statistic; Bounded function; Probability distribution; Cumulative distribution function; Moment-generating function; Inverse-chi-squared distribution; Probability density function; Mathematical analysis; Distribution fitting","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":[0.000281987,0.0001077969,0.0002688677,0.0004220643,0.0001609274,0.00004337143,0.0007872035,0.00002006267,0.0001453567],"category_scores_gemma":[0.0001065918,0.00008728873,0.00008326497,0.0008313134,0.00005939387,0.000122516,0.0001869154,0.0001451663,0.000005561846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000065609,"about_ca_system_score_gemma":0.0001789473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000201006,"about_ca_topic_score_gemma":0.000007982819,"domain_scores_codex":[0.9984117,0.00003089198,0.0005992618,0.0002234924,0.0006425236,0.0000921399],"domain_scores_gemma":[0.998239,0.0001631106,0.0005001762,0.0004408831,0.0004978557,0.0001589959],"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":[0.0001083386,0.0006059358,0.00007235753,0.000009070919,0.0009506174,6.571448e-7,0.0001461788,0.02207888,0.001132108,0.9653515,0.004110691,0.005433726],"study_design_scores_gemma":[0.002749709,0.0005209984,0.002408395,0.00009228742,0.004444753,0.0001038683,0.001159862,0.3267977,0.002872031,0.6134624,0.04459948,0.0007885682],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01217351,0.00005016924,0.9821662,0.004057715,0.000009540448,0.0003599015,0.000972409,0.0000164515,0.0001941017],"genre_scores_gemma":[0.9625773,0.00002090109,0.03608748,0.0003832488,0.00006836916,0.0002458984,0.0002272161,0.00001252778,0.0003771017],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9504037,"threshold_uncertainty_score":0.3559531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030712675867458,"score_gpt":0.3763436009512756,"score_spread":0.2732723333645298,"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."}}