{"id":"W2949739159","doi":"10.48550/arxiv.1309.7589","title":"Linearized FE approximations to a nonlinear gradient flow","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Mathematical Modeling in Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Iterated function; Mathematics; Nonlinear system; Sequence (biology); Applied mathematics; Galerkin method; Flow (mathematics); Balanced flow; Finite element method; Mathematical analysis; Approximations of π; Geometry; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000151761,0.0003385695,0.0003819379,0.0002848952,0.0001119925,0.0001448996,0.001749387,0.0002013442,0.00001638738],"category_scores_gemma":[0.0001565945,0.000378812,0.0001795872,0.0005908992,0.00003958253,0.0003277914,0.002294534,0.0005545336,0.0004339759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000216785,"about_ca_system_score_gemma":0.00008723301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002007814,"about_ca_topic_score_gemma":0.000003175987,"domain_scores_codex":[0.9981729,0.00003877907,0.0002711317,0.0009738434,0.0001204708,0.0004228435],"domain_scores_gemma":[0.9976887,0.0001294061,0.0001199418,0.001517272,0.0002117178,0.0003330231],"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":[0.000002555316,0.00006459527,0.000003417654,0.00007321072,0.00002207239,0.0000237139,0.0002205535,0.8918693,0.0000400493,0.1072113,0.00006586288,0.0004033931],"study_design_scores_gemma":[0.0001927953,0.00002299087,0.00000738349,0.0001116825,0.00002184819,0.000002596594,0.00001216199,0.9181752,0.00006560516,0.08073178,0.0002815182,0.0003744022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02755674,0.00001315016,0.9699382,0.0001687688,0.0004865557,0.0005876091,0.00001117225,0.0006196901,0.0006180853],"genre_scores_gemma":[0.3394719,0.00001340225,0.6596687,0.00008826808,0.00007548631,0.000008356152,0.000009462148,0.00002678096,0.0006376297],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3119152,"threshold_uncertainty_score":0.9998664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06313153911944462,"score_gpt":0.1948897807863464,"score_spread":0.1317582416669018,"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."}}