{"id":"W2912351760","doi":"10.1049/iet-cdt.2018.5055","title":"KBMA: A knowledge‐based multi‐objective application mapping approach for 3D NoC","year":2019,"lang":"en","type":"article","venue":"IET Computers & Digital Techniques","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Computer science; Particle swarm optimization; Network on a chip; Network topology; Computer architecture; Distributed computing; Computer engineering; Embedded system; Machine learning; Computer network","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":[0.0004280274,0.0006270339,0.0004767403,0.0006437562,0.0004066277,0.000604682,0.001174499,0.0009575941,0.002057157],"category_scores_gemma":[0.0008301863,0.000311222,0.0006694596,0.0003963647,0.0003550942,0.0005321649,0.0009646395,0.000610464,0.0002294737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004253626,"about_ca_system_score_gemma":0.0007270451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003040605,"about_ca_topic_score_gemma":0.003170535,"domain_scores_codex":[0.9998322,0.00005322895,0.000007722651,0.00002452842,0.00006236712,0.00001993672],"domain_scores_gemma":[0.999796,0.0001115337,0.00002616564,0.00001489637,0.0000387281,0.00001271149],"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":[0.00003093185,0.00005053332,0.000302837,0.00008740022,0.00006067973,0.00006313727,0.00005727771,0.920553,0.005078995,0.004917558,0.0007413407,0.06805634],"study_design_scores_gemma":[0.000005584437,0.00002909906,0.00006863203,0.000005106721,0.00000623229,0.00001808467,0.000009537573,0.9970259,0.0005506346,0.001552664,0.0007242505,0.000004178826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01815395,0.0003638825,0.9760572,0.0001749452,0.00006660245,0.00009115133,0.00004329434,0.000343848,0.004705284],"genre_scores_gemma":[0.5306433,0.0003360399,0.4631933,0.0002449571,0.00004444083,0.0005473887,0.0001084042,0.00009984378,0.004782252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003040605,"threshold_uncertainty_score":0.006881893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925941181660942,"score_gpt":0.2558613399660693,"score_spread":0.2366019281494599,"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."}}