{"id":"W2915022135","doi":"10.5555/3320516.3320687","title":"Building partial differential equations models using cell-devs","year":2018,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"DEVS; Partial differential equation; Petri net; Ordinary differential equation; Computer science; Cellular automaton; Modelica; Formalism (music); Applied mathematics; Hybrid system; Mathematical optimization; Modeling and simulation; Theoretical computer science; Differential equation; Computational science; Algorithm; Mathematics; Simulation; Mathematical analysis","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":[0.0006583541,0.0004911052,0.0009965911,0.000548924,0.0006385932,0.001043831,0.001281296,0.001201752,0.005013254],"category_scores_gemma":[0.001925069,0.000460171,0.001426426,0.0005480445,0.000607974,0.0005556578,0.00117311,0.001037052,0.0008673043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006941126,"about_ca_system_score_gemma":0.001030386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007984633,"about_ca_topic_score_gemma":0.005762074,"domain_scores_codex":[0.9998006,0.00005319855,0.00001539889,0.00002534428,0.00007979965,0.00002565042],"domain_scores_gemma":[0.9991792,0.0004767257,0.00005659015,0.00007299171,0.0001705054,0.0000440186],"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":[0.0000129729,0.00002643884,0.0007546082,0.00007957051,0.00002548643,0.00009681971,0.00008010936,0.9246545,0.001756795,0.06347294,0.0008580676,0.00818167],"study_design_scores_gemma":[0.000004384081,0.000003342295,0.00003033148,0.000005921042,0.000003366094,0.00001051761,0.000006888057,0.9920493,0.0004383894,0.004410781,0.003033511,0.000003280057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01505314,0.0001865063,0.9719444,0.000202357,0.0001124981,0.00008100214,0.0006433566,0.0008575672,0.01091918],"genre_scores_gemma":[0.3460227,0.0008410451,0.6354197,0.0002842071,0.0001141353,0.001234146,0.00224115,0.0005955547,0.01324751],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007984633,"threshold_uncertainty_score":0.01677102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.333047771524236,"score_gpt":0.4644118350165805,"score_spread":0.1313640634923445,"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."}}