{"id":"W2054270999","doi":"10.1109/eucnc.2014.6882675","title":"Optics in data center: Improving scalability and energy efficiency","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Clos network; Scalability; Computer science; Bandwidth (computing); Optical switch; Optical burst switching; Energy consumption; Efficient energy use; Interconnection; Data center; Electronic engineering; Computer network; Electrical engineering; Optical performance monitoring; Wavelength-division multiplexing; Engineering; Materials science; Optoelectronics","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":[0.0001172029,0.00008010952,0.0001005974,0.0000345488,0.00001459953,0.00001486177,0.0002521879,0.00006127483,0.000004219103],"category_scores_gemma":[0.0001796974,0.00007116754,0.000005538744,0.0001154899,0.00006866931,0.0001251259,0.0003288672,0.00009932894,0.00000165566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001954475,"about_ca_system_score_gemma":0.000001698923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007453223,"about_ca_topic_score_gemma":0.00009049055,"domain_scores_codex":[0.9994155,0.000006604696,0.0001312639,0.00019384,0.00005029677,0.0002024928],"domain_scores_gemma":[0.999351,0.00009285418,0.000006826039,0.0005130243,0.000006392895,0.00002993315],"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":[0.000004263703,0.00006516051,0.008496486,0.00009595428,0.000005850527,0.00000275599,0.00001677534,0.05161381,0.001607259,0.05937224,0.0001388625,0.8785806],"study_design_scores_gemma":[0.0001256473,0.00001211111,0.0007557887,0.000007137721,0.000001212596,0.000001343199,0.00001525208,0.9955073,0.0002566741,0.001864122,0.001357012,0.00009638844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2133716,0.0002399303,0.7759095,0.000107065,0.0001260315,0.00004603197,0.000004694715,0.0009488741,0.00924629],"genre_scores_gemma":[0.9218501,0.00008264987,0.07799866,0.00001735087,0.00001650751,0.000002141756,0.000004624779,0.00001011372,0.00001780988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9438935,"threshold_uncertainty_score":0.2902128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009726281387125394,"score_gpt":0.2141231120487061,"score_spread":0.2043968306615807,"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."}}