{"id":"W2543674304","doi":"10.1103/physreve.94.043323","title":"Renormalized multicanonical sampling in multiple dimensions","year":2016,"lang":"en","type":"article","venue":"Physical review. E","topic":"Theoretical and Computational Physics","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spins; Monte Carlo method; Statistical physics; Scaling; Ising model; Physics; Sampling (signal processing); Hybrid Monte Carlo; Monte Carlo method in statistical physics; Matrix (chemical analysis); Ising spin; Stochastic matrix; Importance sampling; Condensed matter physics; Markov chain Monte Carlo; Materials science; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001313375,0.0003920894,0.0008464581,0.001035698,0.0007590501,0.0007797111,0.001551663,0.0007487665,0.001938973],"category_scores_gemma":[0.004262319,0.000350873,0.0008032454,0.0006745118,0.001747656,0.00137997,0.001128486,0.001138168,0.0002226984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009600976,"about_ca_system_score_gemma":0.0008070105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002203051,"about_ca_topic_score_gemma":0.002333023,"domain_scores_codex":[0.9991847,0.0003810337,0.00003006744,0.0001103453,0.0002287509,0.00006524265],"domain_scores_gemma":[0.9985408,0.0006241762,0.00009071064,0.0004788303,0.0001856879,0.00007979393],"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":[0.0001796782,0.0001056801,0.0007658493,0.0001535265,0.0000637323,0.0002127058,0.0001788633,0.2124328,0.007707104,0.7402653,0.001129824,0.03680493],"study_design_scores_gemma":[0.0000239113,0.0000329398,0.0001682422,0.00001104878,0.000007958086,0.00005563755,0.0000113505,0.8930064,0.001434244,0.1042298,0.0009980712,0.00002025068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2101142,0.0008391439,0.7739059,0.0004279126,0.0002874961,0.0001055478,0.00007414524,0.0008994436,0.01334632],"genre_scores_gemma":[0.7670774,0.0002709784,0.2298138,0.000183471,0.0001350577,0.0001618408,0.00007617054,0.0002218871,0.002059429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002203051,"threshold_uncertainty_score":0.006965995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02501983067231816,"score_gpt":0.3316764976979195,"score_spread":0.3066566670256013,"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."}}