{"id":"W2898624802","doi":"10.1137/1.9781611975482.134","title":"Improved Bounds for Randomly Sampling Colorings via Linear Programming","year":2019,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Engineering and Physical Sciences Research Council","keywords":"Glauber; Mathematics; Combinatorics; Mixing (physics); Discrete mathematics; Conjecture; Constant (computer programming); Graph coloring; Graph; Computer science; Physics","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":[0.009359975,0.004049523,0.003685385,0.002566148,0.002313322,0.005679923,0.007063116,0.002936195,0.01736233],"category_scores_gemma":[0.05157643,0.001786054,0.002584192,0.003301634,0.005006855,0.01188482,0.006512117,0.01317679,0.004007892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006957451,"about_ca_system_score_gemma":0.004074421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003655902,"about_ca_topic_score_gemma":0.005290703,"domain_scores_codex":[0.9914986,0.003267693,0.000205905,0.001661391,0.001805148,0.001561121],"domain_scores_gemma":[0.9337979,0.05472756,0.001833974,0.005294779,0.002685641,0.001660231],"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":[0.0006744905,0.0004687047,0.001739417,0.0006461596,0.0001647169,0.0001821295,0.0004067494,0.331396,0.003163629,0.590247,0.01788279,0.0530283],"study_design_scores_gemma":[0.00003270707,0.00006811123,0.0002196868,0.0000735298,0.00003161208,0.00004360761,0.0000374727,0.7265778,0.0008047687,0.2696642,0.002417348,0.00002916374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03014974,0.003951839,0.8956593,0.004145704,0.0005180638,0.0002634171,0.0008705554,0.002358061,0.06208336],"genre_scores_gemma":[0.5518014,0.004117796,0.3897596,0.003809661,0.001718396,0.001604581,0.002413167,0.002802598,0.04197284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01736233,"threshold_uncertainty_score":0.05808276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1546373306427187,"score_gpt":0.3309351898011887,"score_spread":0.17629785915847,"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."}}