{"id":"W2151546864","doi":"10.1109/tr.2006.874927","title":"On&lt;tex&gt;$q$&lt;/tex&gt;-Logistic and Related Models","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Weibull distribution; Mathematics; Type (biology); Generalized beta distribution; Dirichlet distribution; Beta distribution; Logistic distribution; Univariate; Applied mathematics; Statistics; Log-logistic distribution; BETA (programming language); Distribution (mathematics); Cumulant; Statistical physics; Probability distribution; Logistic regression; Mathematical analysis; Computer science; Distribution fitting; Physics; Multivariate statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004032922,0.0002948692,0.0003420448,0.0001081483,0.0004606952,0.00006138415,0.0001622922,0.0002302967,0.0007521279],"category_scores_gemma":[0.000245251,0.0002775807,0.0001493856,0.000428425,0.0003896768,0.0001313115,0.00000206663,0.0003965493,0.0002327424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002092397,"about_ca_system_score_gemma":0.00006112438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004848477,"about_ca_topic_score_gemma":0.00004016792,"domain_scores_codex":[0.9977832,0.0001458911,0.0007149441,0.0006078018,0.000406498,0.0003416059],"domain_scores_gemma":[0.9970199,0.001718744,0.0001383277,0.000724874,0.0002112666,0.0001868185],"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":[0.00006392243,0.001435366,0.000005001203,0.00008503009,0.00002734926,0.00000325163,0.00005544904,0.03432317,0.0004035833,0.9562151,0.004624346,0.0027584],"study_design_scores_gemma":[0.000759445,0.0001083034,0.001839044,0.00004001766,0.0001213642,0.00001183883,0.00001197633,0.1494406,0.0008046723,0.8461105,0.0004346488,0.0003176373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03997689,0.00001355544,0.940643,0.001130366,0.00019446,0.0006669757,0.0008037341,0.0004113017,0.01615976],"genre_scores_gemma":[0.9902624,0.00001419297,0.007900271,0.0001127314,0.00001799274,0.0002049329,0.00004953263,0.00002679712,0.00141118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9502855,"threshold_uncertainty_score":0.9999676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04347993703119605,"score_gpt":0.3121833279099308,"score_spread":0.2687033908787347,"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."}}