{"id":"W2540699047","doi":"10.1093/imanum/drx045","title":"Numerical methods for motion of level sets by affine curvature","year":2017,"lang":"en","type":"preprint","venue":"IMA Journal of Numerical Analysis","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Nuclear Fuel Cycle and Supply Chain; Fundação para a Ciência e a Tecnologia","keywords":"Curvature; Discretization; Affine transformation; Mathematics; Motion (physics); Partial differential equation; Finite difference; Numerical analysis; Nonlinear system; Finite difference method; Mathematical analysis; Applied mathematics; Geometry; Classical mechanics; 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":[0.001311612,0.0005851061,0.0004630721,0.0008684339,0.0006143647,0.00116816,0.001022697,0.001371228,0.001361963],"category_scores_gemma":[0.00561076,0.000377829,0.0007308933,0.0005502777,0.00183784,0.001389042,0.002035852,0.001449219,0.0004562004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001202372,"about_ca_system_score_gemma":0.0007439205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001493572,"about_ca_topic_score_gemma":0.001089916,"domain_scores_codex":[0.9994857,0.0001482306,0.00003358935,0.00004705427,0.0002525694,0.00003270647],"domain_scores_gemma":[0.9986298,0.0006741496,0.0002174769,0.000180488,0.0002136487,0.00008434959],"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":[0.00005267588,0.00005542901,0.001147045,0.0001772774,0.00004671032,0.0001174155,0.0003184104,0.6097401,0.02039508,0.3419197,0.000827246,0.02520279],"study_design_scores_gemma":[0.000007117224,0.00001074954,0.00006654998,0.000009763103,0.000002937662,0.00002149248,0.000007162825,0.9718149,0.001390375,0.02546164,0.00119859,0.000008655843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02956274,0.0007651225,0.9641974,0.0005189099,0.0001086488,0.0000541121,0.000036094,0.0001395626,0.004617464],"genre_scores_gemma":[0.5660544,0.001053749,0.4228248,0.0002546705,0.0001495881,0.0002773543,0.0001196703,0.0002681437,0.008997557],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001493572,"threshold_uncertainty_score":0.008723855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819631811947466,"score_gpt":0.333709418101753,"score_spread":0.3055130999822783,"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."}}