{"id":"W2050594874","doi":"10.1109/mm.2014.62","title":"Photonic Interconnects for Exascale and Datacenter Architectures","year":2014,"lang":"en","type":"article","venue":"IEEE Micro","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Network topology; Scalability; Router; Computer network; Power budget; Supercomputer; Peering; Latency (audio); Distributed computing; Topology (electrical circuits); Parallel computing; The Internet; Power (physics); Telecommunications; Electrical engineering; Operating system","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":[0.00003980588,0.0001007934,0.0001131746,0.00003552102,0.00002369915,0.0000172969,0.0001216346,0.00005829245,0.00000510311],"category_scores_gemma":[0.00003795241,0.00009060953,0.00002332457,0.00003475088,0.00006079014,0.00003001827,0.0000336647,0.0001052312,0.000008858687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000132206,"about_ca_system_score_gemma":9.79686e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.024986e-7,"about_ca_topic_score_gemma":0.00001357023,"domain_scores_codex":[0.999539,0.000004180948,0.00008283379,0.0001399395,0.00002493341,0.0002091088],"domain_scores_gemma":[0.9996374,0.0001263892,0.000008320902,0.0001911405,0.000007189907,0.00002952042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004278182,0.0000241247,0.0002562888,0.0002930634,0.00007230459,0.000002822727,0.0001884847,0.01471286,0.7853723,0.001037364,0.01309109,0.1849065],"study_design_scores_gemma":[0.0009693161,0.0001471574,0.0002448255,0.0001020092,0.00002205692,0.00002643119,0.00003839625,0.07927183,0.7424795,0.01334837,0.1628777,0.0004723663],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7901906,0.0004610921,0.207936,0.00007764086,0.0002777159,0.0001675836,0.00001359992,0.0004859191,0.0003898666],"genre_scores_gemma":[0.941062,0.00003857632,0.0586183,0.00009879667,0.00007223707,0.00002757599,0.000003830833,0.00002764213,0.00005103332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1844341,"threshold_uncertainty_score":0.3694949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007868991099934126,"score_gpt":0.2153208190960707,"score_spread":0.2074518279961366,"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."}}