{"id":"W3153027315","doi":"10.1214/23-aap2013","title":"Linear-time uniform generation of random sparse contingency tables with specified marginals","year":2024,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Manitoba","funders":"","keywords":"Contingency table; Mathematics; Applied mathematics; Algorithm; Computer science; Statistics; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002289834,0.0006841094,0.0009952722,0.0006263694,0.0006161771,0.001065114,0.001589631,0.0007046078,0.007561244],"category_scores_gemma":[0.01603583,0.0005829503,0.0007798338,0.001048159,0.0009304482,0.001436172,0.001600104,0.001206952,0.001521067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008165746,"about_ca_system_score_gemma":0.001839797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522348,"about_ca_topic_score_gemma":0.002941375,"domain_scores_codex":[0.9975885,0.001074784,0.000139467,0.0004610058,0.000482906,0.0002532758],"domain_scores_gemma":[0.9893135,0.007630283,0.0004131254,0.00180469,0.0005992403,0.0002391536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001796378,0.0002844475,0.004774437,0.0004137546,0.0001722274,0.0004787911,0.0003467354,0.4448808,0.009133354,0.2376517,0.02510309,0.2749643],"study_design_scores_gemma":[0.0002228679,0.00006936537,0.0002539011,0.00001680651,0.00002026293,0.0001236029,0.00002866273,0.8750737,0.004547887,0.1176443,0.001975285,0.00002336708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02393783,0.0001336764,0.9706717,0.0003025786,0.00004850075,0.000174175,0.0005926434,0.002031574,0.002107354],"genre_scores_gemma":[0.3447696,0.0001157618,0.6485332,0.0002836493,0.00006575402,0.0006242978,0.002095874,0.0004088891,0.0031029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007561244,"threshold_uncertainty_score":0.02529484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161756519395485,"score_gpt":0.3646997353871591,"score_spread":0.1485240834476106,"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."}}