{"id":"W2952845365","doi":"10.48550/arxiv.1008.3558","title":"Optimizing the accuracy of Lattice Monte Carlo algorithms for simulating diffusion","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Mathematical Modeling in Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Monte Carlo method; Diagonal; Lattice (music); Algorithm; Boundary value problem; Hybrid Monte Carlo; Statistical physics; Dimension (graph theory); Mathematics; Computer science; Mathematical analysis; Physics; Markov chain Monte Carlo; Geometry; Combinatorics; 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":[0.007380577,0.0006843588,0.0008957125,0.00106018,0.0009073539,0.001988631,0.001714972,0.001938992,0.0009103717],"category_scores_gemma":[0.04887442,0.0005423778,0.0004919443,0.0009884165,0.001713486,0.002482791,0.00144371,0.001711307,0.0003707354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912109,"about_ca_system_score_gemma":0.002059088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007607698,"about_ca_topic_score_gemma":0.005508054,"domain_scores_codex":[0.9963315,0.001620805,0.0002343337,0.0003169999,0.001217372,0.0002789354],"domain_scores_gemma":[0.9825964,0.011995,0.0008474577,0.002341096,0.001900902,0.0003192115],"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":[0.0001160219,0.00004979699,0.001931673,0.00005258943,0.00003147082,0.00002681539,0.00009266872,0.9485303,0.002075232,0.02919436,0.0005048171,0.01739421],"study_design_scores_gemma":[0.000009248412,0.000008160731,0.0000716991,0.000006341483,0.000002589211,0.000005487981,0.000007062793,0.9922059,0.0009779122,0.006474744,0.000226297,0.000004572097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1178069,0.0006664713,0.872613,0.0009682222,0.0001313763,0.00007173989,0.00008125227,0.001094595,0.006566428],"genre_scores_gemma":[0.632871,0.0003436634,0.3650928,0.0001238528,0.00004931069,0.0001121888,0.000103341,0.0002860164,0.001017898],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007607698,"threshold_uncertainty_score":0.0390327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0838646874911191,"score_gpt":0.2322524984080715,"score_spread":0.1483878109169524,"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."}}