{"id":"W2172094915","doi":"10.1109/cicc.1995.518209","title":"A neural network model for propagation delays in systems with high speed VLSI interconnect networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Very-large-scale integration; Interconnection; Artificial neural network; Frame (networking); Convergence (economics); Simple (philosophy); Network topology; Parallel computing; Propagation delay; Electronic engineering; Embedded system; Artificial intelligence; Engineering; 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":[0.0004216623,0.0007130502,0.0004797447,0.0004323753,0.0004995508,0.0008506285,0.001573865,0.001731115,0.002960888],"category_scores_gemma":[0.001676337,0.0004405376,0.0004698022,0.0007851682,0.0006462904,0.001962439,0.0004639319,0.001422744,0.0006113347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001340668,"about_ca_system_score_gemma":0.001022812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01046836,"about_ca_topic_score_gemma":0.00916071,"domain_scores_codex":[0.9997669,0.00004610509,0.00001318586,0.00005007973,0.0000888459,0.00003494862],"domain_scores_gemma":[0.9996612,0.0001602799,0.00004932134,0.00002242838,0.00008994465,0.00001682131],"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":[0.00001718578,0.00001198021,0.0001083421,0.00001873554,0.000006563348,0.00003915427,0.0000154529,0.9793907,0.001460071,0.0145361,0.0002898074,0.004105807],"study_design_scores_gemma":[0.000002532963,0.000006315801,0.00002953332,0.000001424194,0.000002413074,0.000008519924,0.000001189863,0.9968599,0.0001842953,0.002642078,0.0002595705,0.000002273829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02613668,0.0004182616,0.9634112,0.0004135427,0.00009069918,0.00004667788,0.0002325713,0.0004280457,0.008822328],"genre_scores_gemma":[0.832984,0.001687034,0.1224711,0.0002356101,0.0001309243,0.0003754826,0.0004056085,0.000142378,0.04156794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01046836,"threshold_uncertainty_score":0.02081484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589116286456863,"score_gpt":0.181487636390966,"score_spread":0.1655964735263974,"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."}}