{"id":"W2345572700","doi":"10.1137/15m1022744","title":"A New Mixed Formulation and Efficient Numerical Solution of Ginzburg--Landau Equations Under the Temporal Gauge","year":2016,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Research Grants Council, University Grants Committee","keywords":"Mathematics; Curl (programming language); Ode; Galerkin method; Lorenz gauge condition; Ordinary differential equation; Numerical analysis; Finite element method; Discontinuous Galerkin method; Mathematical analysis; Gauge (firearms); Vector field; Maxwell's equations; Sigma; Applied mathematics; Gauge theory; Gauge fixing; Mathematical physics; Differential equation; Physics; Geometry; Quantum mechanics; Gauge boson","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":[],"consensus_categories":[],"category_scores_codex":[0.001104694,0.0001033944,0.0001411719,0.0001532717,0.0003090285,0.0001053689,0.0001131383,0.00003807587,0.00007927955],"category_scores_gemma":[0.0001718534,0.00006007127,0.0000676575,0.0004342606,0.0000570669,0.00006331826,0.00002839557,0.0001476052,0.000009062082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005907335,"about_ca_system_score_gemma":0.00004411953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004343629,"about_ca_topic_score_gemma":0.00000128159,"domain_scores_codex":[0.9987704,0.0001187596,0.0003750977,0.0001463703,0.000354766,0.0002345785],"domain_scores_gemma":[0.9987013,0.0008177403,0.0001283892,0.0001412567,0.00008780403,0.0001234998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004891174,0.00007038141,0.000860829,0.00001958966,0.00005277536,0.000002042056,0.000771741,0.2876111,0.07823347,0.01105525,0.003218145,0.6180558],"study_design_scores_gemma":[0.0006028163,0.0001000592,0.01154711,0.00008606409,0.00001402686,0.00002400571,0.0000406502,0.9811975,0.002038012,0.003059196,0.001177443,0.0001131592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3225355,0.0000926949,0.6758122,0.0006319244,0.0006928151,0.00006814319,9.620608e-7,0.00003368548,0.0001320445],"genre_scores_gemma":[0.9877557,0.000002298664,0.0119375,0.00002360368,0.000128254,3.486441e-7,9.383629e-7,0.00001078035,0.0001405431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6935863,"threshold_uncertainty_score":0.2449635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02668720578115382,"score_gpt":0.2868048205300324,"score_spread":0.2601176147488786,"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."}}