{"id":"W2020947384","doi":"10.1002/jnm.590","title":"Self-adjointS-parameter sensitivities for lossless homogeneous TLM problems","year":2005,"lang":"en","type":"article","venue":"International Journal of Numerical Modelling Electronic Networks Devices and Fields","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lossless compression; Sensitivity (control systems); Homogeneous; Transformation (genetics); Algorithm; Mathematics; Node (physics); Computer science; Applied mathematics; Isomorphism (crystallography); Transmission line; Classification of discontinuities; Transmission (telecommunications); Mathematical optimization; Topology (electrical circuits); Mathematical analysis; Electronic engineering; Physics; Data compression; Combinatorics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002607915,0.0001653687,0.0002758899,0.00009183201,0.0000450382,0.00008139438,0.0001831146,0.0001315065,0.00001854013],"category_scores_gemma":[0.00001504203,0.0001470284,0.0001393408,0.00008520667,0.00002110842,0.0001516783,0.00001827223,0.000399859,9.328838e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000096219,"about_ca_system_score_gemma":0.00002674263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003846977,"about_ca_topic_score_gemma":0.000005551699,"domain_scores_codex":[0.9988075,0.00003815718,0.0004542445,0.0001359104,0.0002196988,0.0003445203],"domain_scores_gemma":[0.9990078,0.0004930827,0.0001307905,0.00007039156,0.0002002515,0.00009765973],"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":[0.00004382835,0.00003752656,0.0000434759,0.0000166983,0.0002062032,0.000003118961,0.0001148221,0.9432097,0.00001120317,0.0005890029,0.0001001014,0.05562434],"study_design_scores_gemma":[0.0004337848,0.0003144045,0.00001495592,0.00003491546,0.00004586676,0.0001582129,0.000008672077,0.9750819,0.00006525225,0.002524263,0.02115231,0.0001654309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0146311,0.004540816,0.9794601,0.0006527068,0.0003723075,0.0001057533,0.000001576433,0.00005682185,0.0001788116],"genre_scores_gemma":[0.9437394,0.001717003,0.05315259,0.0004231186,0.0008961332,0.000006399112,0.000002783307,0.00002688023,0.00003568255],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9291083,"threshold_uncertainty_score":0.5995643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196426846891087,"score_gpt":0.2478487390001826,"score_spread":0.2358844705312717,"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."}}