{"id":"W2909419533","doi":"10.1109/apusncursinrsm.2018.8609408","title":"Characteristic Basis Function Method for the Analysis of Composite Objects Embedded in Layered Media","year":2018,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Impedance parameters; Lossy compression; Basis function; Basis (linear algebra); Matrix (chemical analysis); Block matrix; Electrical impedance; Mathematics; Function (biology); Method of moments (probability theory); Computer science; Boundary (topology); Boundary value problem; Composite number; Algorithm; Mathematical analysis; Geometry; Artificial intelligence; Engineering; Physics; Materials science; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003415424,0.0000962872,0.0002958399,0.0002139702,0.00006703826,0.00002251239,0.0001190362,0.00002159872,0.0009907747],"category_scores_gemma":[0.00001094579,0.00006623402,0.0002215749,0.0008999511,0.00003439417,0.00002983761,0.00001902963,0.00004872417,0.000006039538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008038592,"about_ca_system_score_gemma":0.00001359277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007079789,"about_ca_topic_score_gemma":0.0002494248,"domain_scores_codex":[0.9992397,0.00007407779,0.0002415662,0.0001821095,0.00009564901,0.0001668795],"domain_scores_gemma":[0.999061,0.0004782869,0.0001074776,0.0002349051,0.0000899534,0.00002839854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003343616,0.0005461087,0.1418577,0.00003821016,0.01219478,4.635764e-7,0.005181624,0.001262841,0.4178718,0.004446723,0.0006236017,0.4156418],"study_design_scores_gemma":[0.0007041875,0.0001881414,0.6456569,0.00001483373,0.00474194,1.356236e-7,0.001072127,0.3103991,0.03594814,0.0009199229,0.0001272698,0.0002272298],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6229927,0.00001572912,0.3752996,0.0001245765,0.00006386468,0.0001062092,0.00003069426,0.00001214087,0.001354487],"genre_scores_gemma":[0.9948251,9.391403e-7,0.004575455,0.00006352067,0.0001594686,0.00003517508,0.00006776302,0.0000072562,0.0002652956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5037992,"threshold_uncertainty_score":0.9999225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01098560325639499,"score_gpt":0.2797488781538848,"score_spread":0.2687632748974899,"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."}}