{"id":"W2557270251","doi":"10.1109/lcomm.2016.2634532","title":"Resource Savings in Submarine Networks Using Agility of Filterless Architectures","year":2016,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada); École de Technologie Supérieure","funders":"","keywords":"Control reconfiguration; Computer science; Subsea; Submarine; Computer network; Transceiver; Resource allocation; Resource management (computing); Distributed computing; Telecommunications; Engineering; Wireless; Embedded 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":[0.0001355787,0.0001222695,0.0001827569,0.0001287339,0.00004358169,0.000006821268,0.0008981227,0.00007495556,0.0000049051],"category_scores_gemma":[0.00006863422,0.0001026059,0.00004294837,0.0003191157,0.0004137942,0.0000566919,0.0002217288,0.0002831646,0.000001482994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001081507,"about_ca_system_score_gemma":0.000003504867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002297161,"about_ca_topic_score_gemma":0.0001179622,"domain_scores_codex":[0.9992177,0.00005816199,0.0002985166,0.0001062181,0.00007486423,0.0002444999],"domain_scores_gemma":[0.9978809,0.0004970831,0.00004817733,0.001529828,0.0000168821,0.00002717018],"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":[0.00001333992,0.00003628607,0.01003769,0.00002852239,0.00002772961,0.000001986976,0.0001301894,0.8101895,0.1142441,0.000444821,0.0004433586,0.06440246],"study_design_scores_gemma":[0.002423117,0.00007450409,0.0797156,0.001785071,0.00007779566,0.00003785826,0.0002885624,0.8626049,0.03030566,0.005618512,0.01517859,0.001889814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8477734,0.0003617425,0.1495739,0.001598926,0.00004632934,0.000123591,0.000004944094,0.0002865966,0.0002306396],"genre_scores_gemma":[0.9566104,0.0001087384,0.04309602,0.0001198456,0.00001692633,0.00001736654,0.000002362386,0.00002375615,0.000004617298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.108837,"threshold_uncertainty_score":0.4184148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226233071813758,"score_gpt":0.2483200136139668,"score_spread":0.2260576828958293,"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."}}