{"id":"W2046170949","doi":"10.1142/s0218126610006347","title":"INFLUENCE OF TRAFFIC CORRELATION ON THE PERFORMANCE OF NETWORK-ON-CHIP DESIGNS","year":2010,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Network on a chip; Computer science; Traffic generation model; Point (geometry); Chip; Poisson distribution; Bernoulli's principle; System on a chip; Embedded system; Engineering; Computer network; Telecommunications; Mathematics","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":[0.001303585,0.0001199903,0.000324326,0.0001277686,0.0001067768,0.00007868091,0.0005095992,0.00007666751,9.430671e-7],"category_scores_gemma":[0.00002225446,0.00007558073,0.00009371917,0.0002473866,0.00005819336,0.0002519801,0.00002768243,0.0003287482,0.000001522929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001463168,"about_ca_system_score_gemma":0.00005412809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006130927,"about_ca_topic_score_gemma":0.000001539273,"domain_scores_codex":[0.998463,0.0001582647,0.0007218741,0.0001278181,0.0003731662,0.0001558688],"domain_scores_gemma":[0.9978752,0.0004910873,0.000995461,0.0002746642,0.0002964644,0.00006708999],"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":[0.00001270504,0.00003139906,0.001185853,0.00004517783,0.00003860586,0.000002646219,0.0005952342,0.9659273,0.0006992348,0.02578711,0.0006003221,0.00507441],"study_design_scores_gemma":[0.0004220807,0.001395131,0.01620946,0.001129783,0.00001199117,0.0004047302,0.00006275117,0.9794515,0.000202854,0.00005735751,0.0005175275,0.0001347774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411086,0.00009086532,0.05577746,0.00008289161,0.002616488,0.0001454329,4.392006e-7,0.000008438848,0.0001693302],"genre_scores_gemma":[0.9994022,0.00001611145,0.0001023512,0.00007999404,0.0003741996,0.000001809324,8.605259e-8,0.000005705369,0.00001754435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05829355,"threshold_uncertainty_score":0.3082093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175064226868097,"score_gpt":0.2115791631815638,"score_spread":0.1940727404947541,"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."}}