{"id":"W4226192238","doi":"10.1109/jlt.2022.3168594","title":"Anomaly Prediction With Hybrid Supervised/Unsupervised Deep Learning for Elastic Optical Networks: A Multi-Index Correlative Approach","year":2022,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Optical Network Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Anomaly (physics); Robustness (evolution); Artificial intelligence; Data mining; Artificial neural network; Stability (learning theory); Time series; Unsupervised learning; Machine learning; Data modeling; Scheme (mathematics); Pattern recognition (psychology); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00122588,0.0008779078,0.0007785032,0.001452095,0.000466277,0.0007793271,0.001428162,0.0008503238,0.0006897312],"category_scores_gemma":[0.002452786,0.0003679633,0.0006547279,0.001147567,0.0005715584,0.001711786,0.001135825,0.001282515,0.0001775507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009655893,"about_ca_system_score_gemma":0.0009938949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007094984,"about_ca_topic_score_gemma":0.007903623,"domain_scores_codex":[0.9993516,0.0001207078,0.00004354242,0.0001627206,0.0002089554,0.0001125701],"domain_scores_gemma":[0.9988226,0.0003980989,0.0002041044,0.0001396392,0.0003660467,0.00006955596],"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":[0.0002345042,0.0004191808,0.01465456,0.00006101544,0.0001656992,0.0002135574,0.00009414311,0.7116781,0.005674781,0.004859637,0.002306517,0.2596383],"study_design_scores_gemma":[0.000001240286,0.000008941097,0.0002648476,0.000001425739,0.000003604325,0.000007717503,0.000003184166,0.9983847,0.000418744,0.0008472751,0.00005576739,0.000002560412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09144344,0.000416632,0.9048983,0.0003625172,0.00006495844,0.0000599924,0.0001436281,0.001070737,0.001539829],"genre_scores_gemma":[0.9237704,0.0001870834,0.07338949,0.0001520947,0.00007890973,0.00005768492,0.000300904,0.00004529435,0.002018123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007094984,"threshold_uncertainty_score":0.01410735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008568483487334912,"score_gpt":0.1944682926385491,"score_spread":0.1858998091512142,"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."}}