{"id":"W2950714888","doi":"10.48550/arxiv.1511.01175","title":"Uniform generation of random regular graphs","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Combinatorics; Random regular graph; Graph; Mathematics; Random graph; Discrete mathematics; Running time; Uniform distribution (continuous); Computer science; Pathwidth; Algorithm; Line graph; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002416873,0.0007310508,0.0009923455,0.00178068,0.0007516716,0.001300852,0.002604188,0.001110538,0.003183313],"category_scores_gemma":[0.01568447,0.0007645913,0.00104793,0.0014383,0.001795946,0.002578075,0.002862254,0.001688318,0.001064359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235495,"about_ca_system_score_gemma":0.001090944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00105431,"about_ca_topic_score_gemma":0.001556188,"domain_scores_codex":[0.9975394,0.0008459419,0.0001368808,0.0006433522,0.0006154333,0.0002189996],"domain_scores_gemma":[0.9909022,0.004456458,0.0004022506,0.003146049,0.0008194816,0.0002735248],"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":[0.0007326656,0.0002238725,0.004771801,0.000316167,0.0001081108,0.000359144,0.0004196032,0.3970495,0.02011105,0.3838618,0.01013242,0.1819139],"study_design_scores_gemma":[0.00005730386,0.00004957775,0.0002060588,0.00001486078,0.00001377801,0.0001216829,0.00002174771,0.9047731,0.007207585,0.08515722,0.002359633,0.0000174471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02239602,0.0001089658,0.973938,0.0001522356,0.00003650358,0.0001253413,0.0001836462,0.001033799,0.002025337],"genre_scores_gemma":[0.4111557,0.0001941704,0.58162,0.0004077114,0.0000998512,0.0007751892,0.001480795,0.0005898988,0.003676778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003183313,"threshold_uncertainty_score":0.01278186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040973234407911,"score_gpt":0.1942930800345545,"score_spread":0.09019575659376337,"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."}}