{"id":"W2892287211","doi":"10.1017/s0956792518000554","title":"Time adaptive numerical solution of a highly non-linear degenerate cross-diffusion system arising in multi-species biofilm modelling","year":2018,"lang":"en","type":"article","venue":"European Journal of Applied Mathematics","topic":"Legume Nitrogen Fixing Symbiosis","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Integrator; Degeneracy (biology); Diffusion; Discretization; Applied mathematics; Degenerate energy levels; Finite volume method; Linear system; Mathematics; Statistical physics; Computer science; Physics; Mathematical analysis; Mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001074956,0.0001713959,0.0003795864,0.00004998414,0.0001300836,0.00004318159,0.0003165531,0.00005226204,0.00002688826],"category_scores_gemma":[0.0000243587,0.00007653609,0.0001167913,0.0003134778,0.0001409375,0.00009503602,0.0000998475,0.0001892533,0.00009518848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007027716,"about_ca_system_score_gemma":0.00001245453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007145658,"about_ca_topic_score_gemma":0.000002183458,"domain_scores_codex":[0.9983512,0.00008243309,0.000852732,0.0001652675,0.0003120493,0.0002363544],"domain_scores_gemma":[0.9987083,0.0001032669,0.0007816556,0.00007378592,0.0002479366,0.0000850524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001078283,0.0002175617,0.00005801777,0.00003430555,0.00002504306,0.00002159962,0.001218822,0.0007252276,0.996744,0.0002905185,0.00003429317,0.0005228292],"study_design_scores_gemma":[0.001242489,0.0009482352,0.002314741,0.0008196969,0.00007740329,0.0001019997,0.001822243,0.5498856,0.4421035,0.0001565986,0.00007506413,0.0004523568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749737,0.00002864206,0.02331816,0.00002514554,0.00007216903,0.000130418,0.000008882212,0.00002323587,0.001419614],"genre_scores_gemma":[0.8929632,0.00000451258,0.106689,0.00001274226,0.0002834244,6.200802e-7,0.0000024533,0.000005337883,0.00003876298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5546404,"threshold_uncertainty_score":0.3121051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04423312292379884,"score_gpt":0.2332655655242424,"score_spread":0.1890324426004436,"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."}}