{"id":"W2963165123","doi":"10.5555/3310435.3310569","title":"Improved bounds for randomly sampling colorings via linear programming","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Glauber; Mathematics; Combinatorics; Monomial; Discrete mathematics; Mixing (physics); Conjecture; Graph coloring; Graph; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007446862,0.0002038723,0.0003639421,0.0001061725,0.0001530667,0.0000369558,0.000265816,0.0001395719,0.00002324878],"category_scores_gemma":[0.0002335316,0.0002116663,0.000256265,0.0002709353,0.00004987092,0.0001712688,0.0001045807,0.0001600207,0.000001130768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001044252,"about_ca_system_score_gemma":0.00004790181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003339048,"about_ca_topic_score_gemma":0.00003147536,"domain_scores_codex":[0.9987941,0.00006203208,0.0002048983,0.0004756328,0.00005199516,0.0004112853],"domain_scores_gemma":[0.9984151,0.0007016371,0.0001685948,0.0004227807,0.000166539,0.0001253763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006547227,0.0009402842,0.009544851,0.002555599,0.0009816285,0.00009105774,0.002051525,0.002766944,0.04838303,0.8998408,0.0007578151,0.02553923],"study_design_scores_gemma":[0.02864367,0.001576543,0.00007424294,0.0002814757,0.0008119746,0.00002172234,0.002232777,0.7208014,0.008439403,0.06723763,0.1677661,0.002113056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4932509,0.00001205344,0.504263,0.00002142095,0.0002127775,0.0008140735,0.000004736921,0.0001192715,0.001301807],"genre_scores_gemma":[0.9195954,0.00001133413,0.06869385,0.00005210832,0.000103763,0.000006428381,0.000006907996,0.00004510607,0.01148505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8326032,"threshold_uncertainty_score":0.8631502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1425657100956405,"score_gpt":0.2632740735373573,"score_spread":0.1207083634417168,"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."}}