{"id":"W4249872335","doi":"10.32920/ryerson.14644779","title":"Adaptive time-stepping in the numerical solution of the reaction-diffusion master equation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Time stepping; Master equation; Computer science; Reaction–diffusion system; Diffusion; Computer simulation; Stochastic simulation; Applied mathematics; Scheme (mathematics); Numerical analysis; Mathematical optimization; Statistical physics; Algorithm; Mathematics; Simulation; Mathematical analysis; 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":[0.0008853248,0.0004449452,0.0004967885,0.0003033862,0.0004567907,0.0005224648,0.0008328041,0.0009381755,0.001349256],"category_scores_gemma":[0.002830018,0.0002757325,0.0005362258,0.0005106951,0.0008614791,0.0005801785,0.0006792468,0.001184574,0.0002579334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005834436,"about_ca_system_score_gemma":0.0008525354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005818644,"about_ca_topic_score_gemma":0.003138411,"domain_scores_codex":[0.9997113,0.0001269304,0.00001442153,0.00003300076,0.0000931088,0.00002125725],"domain_scores_gemma":[0.9990401,0.0006888029,0.00007819226,0.0000616005,0.00009527743,0.00003600803],"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":[0.00005837409,0.00002677701,0.0005285054,0.00006504857,0.00002039247,0.00008081872,0.00007100519,0.9548874,0.004739701,0.02717593,0.0003605405,0.01198537],"study_design_scores_gemma":[0.000004592355,0.000005661912,0.00002673992,0.000002166571,0.000001461229,0.000004033575,0.000002173783,0.9974504,0.0003572127,0.0018592,0.0002838359,0.000002440282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05306082,0.0005988101,0.9412053,0.0003663189,0.0001008645,0.00007530703,0.0000467745,0.0002789885,0.004266791],"genre_scores_gemma":[0.5850024,0.000830787,0.409167,0.0001309805,0.00005782061,0.0003630078,0.00009390324,0.000138612,0.004215499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005818644,"threshold_uncertainty_score":0.01156956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456850515488763,"score_gpt":0.2350438789038905,"score_spread":0.2104753737490029,"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."}}