{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001076837,0.0001403828,0.0001949098,0.0001339869,0.0002492408,0.00005065754,0.0000930788,0.00002566342,0.0001145163],"category_scores_gemma":[3.814432e-7,0.0001323306,0.00007895583,0.0002425363,0.00006305453,0.00005754543,0.000002529438,0.00009346812,0.00001962802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001097897,"about_ca_system_score_gemma":0.00001168309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003382858,"about_ca_topic_score_gemma":0.000009071652,"domain_scores_codex":[0.9990907,0.00003583429,0.0002319872,0.0003001677,0.000132595,0.0002086911],"domain_scores_gemma":[0.9995664,0.00002105283,0.00007188343,0.0001782934,0.00008425741,0.0000780921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001466292,0.0008945356,0.001033358,0.00005670979,0.000307964,4.128537e-7,0.003591084,0.01706557,0.8759493,0.0003171384,0.0001336786,0.1005037],"study_design_scores_gemma":[0.0009429456,0.0004146141,0.001722605,0.0001591084,0.0001480629,0.000002137513,0.0006892493,0.8651583,0.1300254,0.0002728074,0.00009313138,0.0003716811],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.423082,0.000003213955,0.5762718,0.0001160621,0.00004802467,0.00009189648,0.00004549918,0.00001727988,0.000324206],"genre_scores_gemma":[0.9909211,0.000001540546,0.008702132,0.0001353346,0.0001098552,0.00001666115,0.00002806305,0.00001499496,0.00007032237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8480927,"threshold_uncertainty_score":0.5396284,"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."}}