{"id":"W2113218666","doi":"10.1109/aps.1995.530098","title":"An efficient higher order numerical convolution for modelling Nth-order Lorentz dispersion","year":2002,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Convolution (computer science); Robustness (evolution); Recursion (computer science); Lorentz transformation; Applied mathematics; Computational electromagnetics; Mathematics; Mathematical analysis; Numerical analysis; Hyperboloid model; Computer science; Electromagnetic field; Algorithm; Physics; Classical mechanics; Geometry","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":[0.00101191,0.0004664764,0.0005534648,0.00043334,0.0004512571,0.000595442,0.000953589,0.0006574008,0.001389697],"category_scores_gemma":[0.00213939,0.0002110253,0.0005998354,0.0005956381,0.0005381295,0.001143512,0.0005202073,0.0007307927,0.0004314114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006205072,"about_ca_system_score_gemma":0.0009592719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001562902,"about_ca_topic_score_gemma":0.001587084,"domain_scores_codex":[0.9995864,0.00009479066,0.00002514241,0.00003420196,0.0002320146,0.00002751419],"domain_scores_gemma":[0.9991224,0.000429886,0.00007538031,0.0001717435,0.000165823,0.00003472569],"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":[0.00019847,0.0001727826,0.001426663,0.0002521426,0.00006831153,0.0003908896,0.0003382311,0.5194475,0.1132615,0.1647161,0.001736194,0.1979911],"study_design_scores_gemma":[0.000004053389,0.00002094817,0.00008704198,0.00000526244,0.000007290508,0.00008591657,0.000005171105,0.9863885,0.00938311,0.002246051,0.001758859,0.000007768725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007114319,0.00006529349,0.9914958,0.00002246799,0.00002504041,0.00001895779,0.00001024787,0.0001850807,0.001062701],"genre_scores_gemma":[0.1595025,0.0002071609,0.8373181,0.00002933264,0.0000265055,0.00009251248,0.00005698811,0.0001069107,0.002659989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001562902,"threshold_uncertainty_score":0.005351543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961246505312772,"score_gpt":0.2671519245396656,"score_spread":0.2375394594865379,"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."}}