{"id":"W2569360723","doi":"10.1002/cjs.11305","title":"Bayesian multiplicity control for multiple graphs","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Graphical model; Computer science; Random graph; Conditional independence; Statistical inference; Bayesian inference; Gibbs sampling; Exponential random graph models; Graph; Algorithm; Bayesian probability; Mathematics; Theoretical computer science; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006915404,0.0001454521,0.0004031091,0.0001153077,0.0005749737,0.0001853311,0.0004593749,0.0000821147,0.0001223093],"category_scores_gemma":[0.02862151,0.0001277421,0.00009705257,0.00002662072,0.0002534158,0.00008840408,0.000008720911,0.000210672,0.000002753316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007070031,"about_ca_system_score_gemma":0.000593411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173683,"about_ca_topic_score_gemma":0.01916544,"domain_scores_codex":[0.9987722,0.00006168826,0.0005208731,0.0001175403,0.0001552008,0.0003724767],"domain_scores_gemma":[0.9946089,0.002955736,0.0006570147,0.0003396717,0.0006728683,0.000765808],"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":[0.00006851618,0.00003370435,0.02736577,0.0001186053,0.0001196646,0.0001634696,0.0002458972,0.000008073899,0.0000948618,0.8999166,0.02533611,0.04652875],"study_design_scores_gemma":[0.001946492,0.0002472567,0.02447886,0.00008151567,0.0001245211,0.00003740043,0.00006004884,0.01102032,0.00009162347,0.9581425,0.003568996,0.0002004559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003334989,0.00002990549,0.992043,0.0002729406,0.0006063567,0.0002280596,0.003034988,0.000004211641,0.0004455259],"genre_scores_gemma":[0.5198993,0.000003166983,0.4798528,0.00008236322,0.00009273647,0.000003731823,0.000001562099,0.00001532131,0.00004897213],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5165643,"threshold_uncertainty_score":0.9987322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0973279761351604,"score_gpt":0.3438506611979792,"score_spread":0.2465226850628188,"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."}}