{"id":"W2046133867","doi":"10.1117/12.566468","title":"Time domain simulation of photonic crystals using the transmission line matrix method","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Finite-difference time-domain method; Photonic crystal; Transmission-line matrix method; Polygon mesh; Computer science; Transmission line; Computation; Grid; Plane wave expansion; Computational science; Matrix (chemical analysis); Transmission (telecommunications); Plane wave expansion method; Electronic engineering; Algorithm; Computational electromagnetics; Optics; Materials science; Mathematics; Physics; Telecommunications; Geometry; 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":[0.0009569217,0.000303457,0.0004652152,0.0001088393,0.00007667523,0.00004666985,0.0005972234,0.0002042229,0.00003360974],"category_scores_gemma":[0.0002763352,0.0002256685,0.0005766728,0.0005343932,0.0001674111,0.0002853809,0.00005716981,0.0003092909,8.127647e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791281,"about_ca_system_score_gemma":0.00003077319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005672261,"about_ca_topic_score_gemma":2.732066e-8,"domain_scores_codex":[0.9979456,7.202168e-8,0.0008321762,0.0002613501,0.0006132288,0.0003475761],"domain_scores_gemma":[0.9984141,0.0003902861,0.0002771755,0.00007017368,0.0007465071,0.0001017515],"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":[0.00005433437,0.00004820832,0.000004791013,0.0002619462,0.0001754156,3.335863e-8,0.0003166239,0.29943,0.6740252,0.02513534,0.00003579113,0.0005122959],"study_design_scores_gemma":[0.0008619237,0.0002143174,0.00003953459,0.0001911881,0.0001178493,0.000007714969,0.0003032336,0.7019793,0.2920455,0.003335722,0.0007039958,0.0001997255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9707778,0.000247093,0.02702098,0.0006301167,0.0001080627,0.0005630106,0.00001854182,0.000114439,0.0005199398],"genre_scores_gemma":[0.4390944,0.00005563331,0.5605018,0.00003380179,0.0001613049,0.00003215544,0.000003983815,0.0000665606,0.00005041472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5334808,"threshold_uncertainty_score":0.9202496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382776006304247,"score_gpt":0.2848697972336223,"score_spread":0.2710420371705798,"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."}}