{"id":"W2800451635","doi":"10.6000/1929-6029.2018.07.02.4","title":"Bayesian Analysis of Markov Based Logistic Model","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Bayes factor; Exponential function; Statistics; Mathematics; Bayes' theorem; Variable-order Bayesian network; Bayes estimator; Function (biology); Applied mathematics; Logistic regression; Markov model; Bayesian inference; Econometrics; Computer science; Markov chain","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008450503,0.0001113389,0.000520042,0.001597842,0.0000384004,0.00004139866,0.001071514,0.0001338037,0.003381553],"category_scores_gemma":[0.0765898,0.00008901742,0.0001077832,0.0009687527,0.001007792,0.00005490334,0.0001353742,0.0007978546,0.000004822938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001823309,"about_ca_system_score_gemma":0.0008223873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006403915,"about_ca_topic_score_gemma":0.0001855386,"domain_scores_codex":[0.9932555,0.0006204579,0.001244525,0.0001806806,0.004378817,0.0003200117],"domain_scores_gemma":[0.9806894,0.01465414,0.0003613232,0.0002051694,0.003797138,0.0002928429],"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":[0.0004638407,0.0005772584,0.003785778,0.00006729808,0.0006444255,0.0007085564,0.0002389986,0.0001189025,0.00009673102,0.8433061,0.004239039,0.1457531],"study_design_scores_gemma":[0.0003798403,0.0001684061,0.001673043,0.0001464233,0.00007110479,0.000007093152,0.00003500997,0.4950002,0.00007795895,0.502344,0.00004557757,0.00005137016],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001961496,0.00001570489,0.9943508,0.0007599983,0.0002468037,0.00006314137,0.0002666165,0.000003287786,0.00233219],"genre_scores_gemma":[0.4639419,0.00003150727,0.5357864,0.00007620469,0.0001252418,0.000002013709,0.00000478834,0.000008169589,0.00002378594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4948812,"threshold_uncertainty_score":0.9975295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2158784806776032,"score_gpt":0.5573796766179351,"score_spread":0.3415011959403319,"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."}}