{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003355722,0.0005113361,0.000368277,0.0003614642,0.0002180796,0.000609902,0.0004184,0.0008461774,0.001336184],"category_scores_gemma":[0.001575271,0.0001735203,0.000260082,0.0001544788,0.0008911429,0.000621166,0.0009717335,0.0006045022,0.0002154114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003645313,"about_ca_system_score_gemma":0.0003040006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002196379,"about_ca_topic_score_gemma":0.001059503,"domain_scores_codex":[0.9998561,0.00005034107,0.000005273985,0.00002041799,0.00005385682,0.00001400491],"domain_scores_gemma":[0.9996823,0.0001542157,0.0000617602,0.00003107456,0.00004865561,0.00002202025],"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":[0.00006079364,0.0000424525,0.000565194,0.00008917289,0.00001700139,0.0001134655,0.0001535089,0.8884957,0.01680471,0.08166751,0.0005249513,0.01146538],"study_design_scores_gemma":[0.000002215051,0.00001053191,0.0000464805,0.000003631151,0.000001138651,0.00001183478,0.000005732505,0.9951082,0.000825739,0.003565242,0.000416346,0.000002863636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09467866,0.0003651557,0.8919288,0.0005461678,0.00006287206,0.0000466313,0.00006901288,0.0002425498,0.01206011],"genre_scores_gemma":[0.877009,0.000357909,0.1040048,0.0001662871,0.00007747093,0.0001042359,0.0001112817,0.00008491908,0.01808417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002196379,"threshold_uncertainty_score":0.004469991,"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."}}