{"id":"W2149283958","doi":"10.1109/aps.2009.5171726","title":"Hardware accelerated symmetric condensed node TLM procedure for NVIDIA graphics processing units","year":2009,"lang":"en","type":"article","venue":"Digest - IEEE Antennas and Propagation Society. International Symposium","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Graphics; Laptop; Computer science; Workstation; Graphics hardware; Node (physics); Computer graphics (images); General-purpose computing on graphics processing units; Parallel computing; Computer hardware; Computational science; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005031214,0.0005457451,0.0005696529,0.0005684369,0.0006536109,0.0005522691,0.001249913,0.0003951668,0.03576544],"category_scores_gemma":[0.002246612,0.0002250412,0.0003743626,0.0007658015,0.0002980849,0.0004542776,0.0008223476,0.001000074,0.006430278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005479928,"about_ca_system_score_gemma":0.001892747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002819764,"about_ca_topic_score_gemma":0.005112679,"domain_scores_codex":[0.9995909,0.0001115315,0.00001906083,0.00003147872,0.0002052495,0.00004175475],"domain_scores_gemma":[0.9988708,0.0002522889,0.0000767434,0.0002167178,0.0005374848,0.00004598028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008459043,0.0002015281,0.002853274,0.000604561,0.00009729523,0.0007261184,0.0009501992,0.07237147,0.0863635,0.07655033,0.1583089,0.6001268],"study_design_scores_gemma":[0.0003322418,0.0002271694,0.001664688,0.00004916019,0.0000339199,0.0004077888,0.0002240076,0.7894997,0.05982934,0.01316696,0.134491,0.00007401625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01484827,0.00007164968,0.9614819,0.0002176063,0.0001818596,0.0002465798,0.0005476214,0.008223166,0.01418138],"genre_scores_gemma":[0.09084339,0.00005754414,0.8938643,0.0001315461,0.0000338816,0.0005075251,0.001234005,0.002132032,0.0111957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03576544,"threshold_uncertainty_score":0.1196473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819953457670823,"score_gpt":0.2837600850273942,"score_spread":0.255560550450686,"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."}}