{"id":"W2615697992","doi":"10.1007/s10915-017-0483-y","title":"Time Adaptive Numerical Solution of a Highly Degenerate Diffusion–Reaction Biofilm Model Based on Regularisation","year":2017,"lang":"en","type":"article","venue":"Journal of Scientific Computing","topic":"Bacterial biofilms and quorum sensing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Discretization; Reaction–diffusion system; Degeneracy (biology); Degenerate energy levels; Singularity; Applied mathematics; Ordinary differential equation; Diffusion; Mathematical analysis; Differential equation; Physics","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":[0.001068101,0.0007410121,0.001259961,0.0005370557,0.0005598824,0.001059937,0.00139988,0.003221043,0.001400697],"category_scores_gemma":[0.004010697,0.0006103023,0.0008776677,0.0004107894,0.001941793,0.0007633017,0.002077343,0.001529558,0.0001713278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008758854,"about_ca_system_score_gemma":0.001210058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008566828,"about_ca_topic_score_gemma":0.003639784,"domain_scores_codex":[0.9995989,0.0001744317,0.00002067401,0.00006562434,0.00008523335,0.00005522473],"domain_scores_gemma":[0.9982186,0.001155338,0.0002003403,0.0000790061,0.0002066884,0.0001399352],"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":[0.00007153088,0.0000398236,0.0004078322,0.00005279857,0.00002663674,0.00009373325,0.00006840793,0.9819716,0.002209362,0.01113847,0.0002565093,0.003663226],"study_design_scores_gemma":[0.0000056724,0.000007467338,0.00002310871,0.000001495409,0.000001542991,0.000004398571,0.000002889662,0.9991571,0.00009314249,0.000641435,0.00005899879,0.000002822782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1436058,0.0005718629,0.8441098,0.001184562,0.0003502129,0.00009553592,0.00008019378,0.0001909437,0.00981103],"genre_scores_gemma":[0.882313,0.0002182933,0.1094796,0.0001924076,0.00008135047,0.0002001908,0.000094784,0.0000903257,0.007330144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008566828,"threshold_uncertainty_score":0.01703393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819087926116797,"score_gpt":0.2451563914670873,"score_spread":0.2269655122059193,"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."}}