{"id":"W171351407","doi":"10.48550/arxiv.1401.1977","title":"Robust Energy Management for Green and Survivable IP Networks","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Backup; Computer science; Robustness (evolution); Computer network; Energy consumption; Survivability; Spare part; Quality of service; Network planning and design; Distributed computing; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009328008,0.000316739,0.0003402343,0.000139068,0.00007803539,0.00002856374,0.0004648917,0.0004078275,0.000007655001],"category_scores_gemma":[0.000007416079,0.0003833181,0.00009647204,0.0001936946,0.000109661,0.0000806501,0.0008215937,0.000334243,0.000003471611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001167364,"about_ca_system_score_gemma":0.000004196535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003081965,"about_ca_topic_score_gemma":0.00006378812,"domain_scores_codex":[0.9988012,0.00001700234,0.0001460091,0.0005780437,0.00003388572,0.0004238316],"domain_scores_gemma":[0.9991053,0.0001130295,0.00005391037,0.0005980706,0.00003932474,0.00009038125],"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":[0.00001470631,0.000007205627,0.0001176675,0.0001832951,0.000122868,0.00002564074,0.0000019639,0.8752654,6.300299e-7,0.119169,0.0004832759,0.004608311],"study_design_scores_gemma":[0.0003015369,0.00002247136,0.0001078781,0.00008135133,0.0001015919,7.606669e-7,0.00002295593,0.9444558,0.000007338459,0.04836823,0.006130826,0.0003992473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01064617,0.0003578203,0.9832671,0.00001816828,0.0003160088,0.0002636574,0.00001014047,0.0009504157,0.004170554],"genre_scores_gemma":[0.9820448,0.002612364,0.01296533,0.00002514946,0.0001188344,0.000007659257,0.00003550028,0.00006682098,0.002123526],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9713987,"threshold_uncertainty_score":0.9998619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04123985693859327,"score_gpt":0.1548021678919984,"score_spread":0.1135623109534051,"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."}}