{"id":"W1990658860","doi":"10.1103/physreve.70.015103","title":"Building reliable lattice Monte Carlo models for real drift and diffusion problems","year":2004,"lang":"en","type":"article","venue":"Physical Review E","topic":"Theoretical and Computational Physics","field":"Physics and Astronomy","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Monte Carlo method; Statistical physics; Lattice (music); Random walk; Monte Carlo molecular modeling; Hybrid Monte Carlo; Monte Carlo method in statistical physics; Diffusion; Kinetic Monte Carlo; Computer science; Algorithm; Markov chain Monte Carlo; Mathematics; Physics; Statistics","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":[0.003086023,0.000783693,0.001520996,0.001039554,0.0007414207,0.002038295,0.002706369,0.002323949,0.002373781],"category_scores_gemma":[0.01437356,0.001008453,0.0008906565,0.0007705938,0.002474084,0.002661333,0.001802874,0.002162139,0.0005395432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436315,"about_ca_system_score_gemma":0.00156465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004325198,"about_ca_topic_score_gemma":0.004033098,"domain_scores_codex":[0.9988233,0.0006279893,0.00005035683,0.00009503879,0.0003089644,0.00009426234],"domain_scores_gemma":[0.9936584,0.004334154,0.0004871285,0.0006413628,0.0005731456,0.0003058252],"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":[0.00001270239,0.00001375042,0.0001442294,0.00002061812,0.000009417534,0.00002052301,0.00002853311,0.9470119,0.0001674518,0.05089376,0.0001721556,0.001504982],"study_design_scores_gemma":[0.000003702638,0.000002327379,0.000004688601,0.000002095133,8.172321e-7,0.000001902132,0.000001905179,0.986822,0.00004137596,0.01298023,0.0001371969,0.000001798371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04497883,0.000325891,0.9497374,0.000595163,0.00008136559,0.00005514446,0.00009803329,0.0004362829,0.003691809],"genre_scores_gemma":[0.5286119,0.0005949754,0.4660884,0.0001749661,0.0001121653,0.0004822445,0.00027759,0.0003054132,0.00335237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004325198,"threshold_uncertainty_score":0.01632071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376411752065258,"score_gpt":0.2807123000649679,"score_spread":0.2669481825443153,"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."}}