{"id":"W3197983502","doi":"10.1007/978-3-030-63591-6_28","title":"Solving Cardiac Bidomain Problems with B-spline Adaptive Collocation","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discretization; Collocation (remote sensing); Ordinary differential equation; B-spline; Collocation method; Mathematics; Partial differential equation; Applied mathematics; Software package; Discretization error; Software; Mathematical analysis; Algorithm; Computer science; Differential equation","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005316606,0.0008004975,0.001209836,0.000333158,0.00008938802,0.0001325893,0.0003409889,0.0003593992,0.00006304987],"category_scores_gemma":[0.0003871978,0.0008199788,0.00009973067,0.0002192655,0.0001880537,0.0001609481,0.0001590346,0.001000319,0.00004258449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000587695,"about_ca_system_score_gemma":0.0001106872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.662815e-7,"about_ca_topic_score_gemma":0.000004905761,"domain_scores_codex":[0.9968824,0.000009260827,0.001154805,0.0005782755,0.0008558135,0.0005194893],"domain_scores_gemma":[0.9974748,0.0008912834,0.0005082561,0.000304308,0.0006710321,0.0001503029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001076288,0.00007775777,0.000009654336,0.005359541,0.000304995,0.00004790015,0.001736947,0.02299664,0.0001878688,0.9629495,0.0005060859,0.005812384],"study_design_scores_gemma":[0.0003763197,0.0001320566,0.00001095564,0.00372556,0.0002554701,0.00002899233,0.0002792882,0.2176941,0.0002316056,0.7684194,0.007525105,0.001321121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007552342,0.0007789966,0.9022921,0.0000142409,0.0002716222,0.001073248,0.000194402,0.0003470354,0.09495282],"genre_scores_gemma":[0.0004915,0.0006030567,0.9823572,0.00001551393,0.0002096901,0.0001589004,0.00006694636,0.0004439291,0.01565327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1946975,"threshold_uncertainty_score":0.9994251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162019546211418,"score_gpt":0.2475464110344028,"score_spread":0.2259262155722886,"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."}}