{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003094464,0.0002650965,0.0003316863,0.000232733,0.0001163758,0.0005820955,0.0008494565,0.0001482231,7.155174e-7],"category_scores_gemma":[0.0000119085,0.0002519923,0.0002069571,0.0004325999,0.00003896752,0.0008544875,0.0002255101,0.0001514828,0.00003498443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001477333,"about_ca_system_score_gemma":0.00005812987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001182345,"about_ca_topic_score_gemma":0.000001277946,"domain_scores_codex":[0.9983003,0.0000445508,0.0003850288,0.0007390542,0.0001708233,0.0003602172],"domain_scores_gemma":[0.9986143,0.0001785125,0.0001921891,0.0006513944,0.0002817111,0.0000819168],"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":[0.00006729749,0.001243082,0.001285459,0.0005041619,0.0001745653,0.000003675468,0.002496134,0.00186091,0.002133067,0.05031776,0.01949611,0.9204178],"study_design_scores_gemma":[0.0003847005,0.0002250906,0.00007247584,0.00009907991,0.000002946698,0.00001831315,0.00004251816,0.9621558,0.001865158,0.0006962308,0.03408189,0.0003558164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004788603,0.00005183448,0.9845026,0.00006954271,0.0004719855,0.001869451,0.00001080955,0.001339322,0.01120559],"genre_scores_gemma":[0.5729601,7.748363e-7,0.4260326,0.0001699893,0.0001607419,0.0004048123,0.0000294608,0.00002098966,0.0002205256],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9602948,"threshold_uncertainty_score":0.9999932,"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."}}