{"id":"W2305951159","doi":"10.1016/j.cam.2016.03.002","title":"Efficient numerical differentiation of implicitly-defined curves for sparse systems","year":2016,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Toronto; Canada Foundation for Innovation","keywords":"Mathematics; Discretization; Numerical analysis; Applied mathematics; Tracing; Mathematical optimization; Mathematical analysis; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000156176,0.00009402192,0.0002596295,0.00007375272,0.00002391452,0.00001320063,0.00006260549,0.00002797927,0.000003113534],"category_scores_gemma":[0.00002376114,0.00006303991,0.00006338212,0.00006313968,0.00001989381,0.0000197513,0.00001301096,0.00003679073,7.83848e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002202035,"about_ca_system_score_gemma":0.00002024764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.320941e-7,"about_ca_topic_score_gemma":6.217056e-8,"domain_scores_codex":[0.9990979,0.000004048425,0.0005371121,0.00005397521,0.0002239353,0.00008302982],"domain_scores_gemma":[0.998971,0.0005401444,0.0002177474,0.00004043304,0.0001841952,0.00004645924],"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":[0.0000121484,0.00007453409,0.00001575947,0.0005932161,0.00006515207,2.319214e-7,0.00005538227,0.8446392,0.003141451,0.1497517,0.0002061059,0.001445199],"study_design_scores_gemma":[0.0005484896,0.00003852855,0.001190258,0.0002818399,0.00003658044,0.00002612219,0.00001950235,0.9655741,0.00007189562,0.03210889,0.0000222254,0.00008152932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3184155,0.0001656054,0.6810452,0.00007090622,0.00009348692,0.0001094297,0.00002902585,0.000009677874,0.00006116373],"genre_scores_gemma":[0.9580926,0.00004190635,0.04176904,0.000009518258,0.00005401924,0.000005386121,0.000006384509,0.00001380089,0.000007404709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6396771,"threshold_uncertainty_score":0.2570693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009209637165629071,"score_gpt":0.212132126001403,"score_spread":0.2029224888357739,"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."}}