{"id":"W2492739052","doi":"10.1109/icc.2016.7511040","title":"Scalable architecture and low-latency scheduling schemes for next generation photonic datacenters","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Photonics; Scheduling (production processes); Scalability; Network packet; Latency (audio); Computer network; Transmission delay; Materials science; Engineering; Optoelectronics; Telecommunications","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":[0.00004156644,0.0001198066,0.0001078981,0.00004495496,0.00004871166,0.0000324862,0.00009677205,0.00008424416,0.00002518196],"category_scores_gemma":[0.000069817,0.000079832,0.00002169459,0.00007064239,0.00005450162,0.0002571902,0.00004843985,0.00007179998,0.000008677544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000030771,"about_ca_system_score_gemma":0.000004245445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.316768e-7,"about_ca_topic_score_gemma":0.00000582884,"domain_scores_codex":[0.9993832,0.000002612447,0.0001209817,0.0001859405,0.00005052331,0.0002567289],"domain_scores_gemma":[0.9996772,0.00006274592,0.00001161624,0.0001901453,0.00001770099,0.00004060314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001079597,0.00000910128,0.0001436997,0.0000749691,0.00003264551,8.972809e-7,0.000012168,0.01777791,0.7067835,0.006267059,0.0004427269,0.2684446],"study_design_scores_gemma":[0.0008946953,0.00006363885,0.00002936846,0.0001397262,0.00001565935,0.000006432777,0.00004506683,0.7518044,0.2287182,0.005534783,0.01234707,0.0004009953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.331052,0.0005997436,0.6670474,0.0002731197,0.00009669711,0.0001815811,0.000006106408,0.0005820825,0.0001613143],"genre_scores_gemma":[0.6104264,0.0004860022,0.3887967,0.000035584,0.00006337102,0.00003917974,0.000007278698,0.00002552865,0.0001199294],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7340264,"threshold_uncertainty_score":0.3255455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085437255560435,"score_gpt":0.228869221564553,"score_spread":0.2080148490089486,"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."}}