{"id":"W2128834968","doi":"10.1109/tap.2006.879194","title":"Dispersion Properties and Applications of the Coifman Scaling Function Based S-MRTD","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Dispersion (optics); Function (biology); Basis function; Scaling; Domain (mathematical analysis); Applied mathematics; Algorithm; Mathematical analysis; Mathematics; Physics; Optics; 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":[0.000343217,0.000226156,0.0002044808,0.0004497613,0.000257128,0.0004560065,0.000312716,0.000698253,0.001035063],"category_scores_gemma":[0.001292954,0.000125283,0.0002381892,0.0004501351,0.0005104092,0.0004950226,0.0002863248,0.0003358104,0.0002350656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004071002,"about_ca_system_score_gemma":0.0003095173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008400208,"about_ca_topic_score_gemma":0.0006294402,"domain_scores_codex":[0.9998186,0.0000391011,0.000009053101,0.00001841416,0.0001014725,0.00001334401],"domain_scores_gemma":[0.9993749,0.0003419154,0.00006768166,0.00006709663,0.0001323895,0.00001601195],"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":[0.0001511532,0.00006605952,0.00171369,0.000191443,0.00001790157,0.0008521884,0.0004967982,0.4412751,0.1449181,0.2421,0.002384604,0.1658331],"study_design_scores_gemma":[0.000007393166,0.00003589743,0.0002467099,0.00001255604,0.000004198646,0.0003530574,0.00002416991,0.9716908,0.01604937,0.006915074,0.004647451,0.00001331793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05994646,0.0009679819,0.9117417,0.0003687751,0.00006257463,0.00002812836,0.000025311,0.0004075603,0.02645147],"genre_scores_gemma":[0.681288,0.0008039373,0.3130651,0.00007943116,0.00004436181,0.00004750104,0.000032139,0.00007336093,0.004566223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001035063,"threshold_uncertainty_score":0.003462613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156039456911107,"score_gpt":0.2024751114803374,"score_spread":0.1909147169112264,"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."}}