{"id":"W2127266781","doi":"10.1109/tmtt.2003.822034","title":"An Adjoint Variable Method for Time-Domain TLM With Wide-Band Johns Matrix Boundaries","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Classification of discontinuities; Matrix (chemical analysis); Transmission-line matrix method; Mathematics; Transpose; Time domain; Function (biology); Transmission line; Computational electromagnetics; Mathematical analysis; Sensitivity (control systems); Adjoint equation; Domain (mathematical analysis); Algorithm; Applied mathematics; Computer science; Physics; Electromagnetic field; Electronic engineering; Partial differential equation; Engineering; Eigenvalues and eigenvectors; Telecommunications","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":[0.0006943714,0.0004128213,0.0003666006,0.0002813243,0.0002968156,0.0004284904,0.0005488835,0.000605642,0.001538746],"category_scores_gemma":[0.001406522,0.0002818615,0.0003223053,0.0002430418,0.0004706066,0.0005763997,0.0006433238,0.0008790222,0.0003197912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003356137,"about_ca_system_score_gemma":0.0006252251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008693823,"about_ca_topic_score_gemma":0.001164891,"domain_scores_codex":[0.99981,0.00007164991,0.000006424362,0.00001705879,0.0000823644,0.0000124992],"domain_scores_gemma":[0.9995798,0.0002582609,0.00004336598,0.00003567389,0.00006559774,0.0000171691],"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":[0.00007204522,0.00004494004,0.0004031349,0.00005970674,0.00002454818,0.00008282175,0.00009165013,0.8306412,0.01884381,0.05818198,0.0007577892,0.09079642],"study_design_scores_gemma":[0.000004362828,0.000009141328,0.00001996459,0.000002370638,0.000001369989,0.000009018945,0.000003043434,0.9952697,0.001347124,0.00270383,0.0006268337,0.000003168083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001686675,0.000009748343,0.9979012,0.00001454968,0.000004846072,0.000007070508,0.00000440547,0.00009324294,0.0002782088],"genre_scores_gemma":[0.1140486,0.00003691606,0.8844166,0.00003810719,0.000009425645,0.0001301163,0.00004033529,0.00008772259,0.001192061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001538746,"threshold_uncertainty_score":0.005147636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005471801262550511,"score_gpt":0.265846012752432,"score_spread":0.2603742114898815,"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."}}