{"id":"W2773630892","doi":"10.1109/tap.2018.2866509","title":"A Macromodeling Approach to Efficiently Compute Scattering from Large Arrays of Complex Scatterers","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Method of moments (probability theory); Integral equation; Scattering; Equivalence (formal languages); Surface (topology); Reduction (mathematics); Speedup; Current density; Computational electromagnetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001944466,0.0005124642,0.0004435765,0.0004000343,0.0003055501,0.0004402068,0.0006789902,0.0007253028,0.00248394],"category_scores_gemma":[0.0006563278,0.0002878821,0.0005860295,0.0005042086,0.0002439084,0.0006828285,0.0003955565,0.0008289065,0.000872149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003427723,"about_ca_system_score_gemma":0.0009163119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002818367,"about_ca_topic_score_gemma":0.004546304,"domain_scores_codex":[0.9999043,0.00001784174,0.000004852707,0.00001410347,0.00004759313,0.000011366],"domain_scores_gemma":[0.9998185,0.00007579201,0.00001834653,0.00002994576,0.0000451867,0.00001222831],"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":[0.00002468503,0.00006095049,0.0004992209,0.0000800739,0.00003507125,0.00008484464,0.00009800059,0.8736094,0.02815711,0.02700632,0.001699275,0.06864499],"study_design_scores_gemma":[0.000003937534,0.000006985807,0.00003077595,0.000001947186,0.000002568311,0.00001873449,0.000004983085,0.9947836,0.001160727,0.002166111,0.001816641,0.000003006978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004520499,0.00004817666,0.9933749,0.00004649322,0.00001561583,0.0000152467,0.00003155585,0.0004199409,0.001527703],"genre_scores_gemma":[0.1724989,0.0002742191,0.8209795,0.0001107349,0.00005097216,0.0001968747,0.0002740225,0.0003474769,0.005267364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002818367,"threshold_uncertainty_score":0.008309603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763873757019782,"score_gpt":0.2440908738482893,"score_spread":0.2264521362780915,"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."}}