{"id":"W4385451896","doi":"10.1109/iccns58795.2023.10193494","title":"Importance of Optical Metrics for IGP Configuration Change Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada); Carleton University","funders":"","keywords":"Computer science; Default gateway; Optical power; Feature (linguistics); Precision and recall; Recall; Artificial intelligence; Power (physics); Data mining; Real-time computing; Machine learning; Computer network; Optics","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":[0.00181382,0.001378203,0.0007898915,0.003403689,0.0003773944,0.001200426,0.0006654188,0.0007210294,0.0007214102],"category_scores_gemma":[0.009993892,0.0002565523,0.0005028555,0.001582897,0.0003784767,0.001306411,0.0005653409,0.00112163,0.0004964406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007854578,"about_ca_system_score_gemma":0.0005356373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006488257,"about_ca_topic_score_gemma":0.006324788,"domain_scores_codex":[0.99841,0.0003061706,0.000139496,0.0004276774,0.0005292176,0.0001875025],"domain_scores_gemma":[0.9936259,0.003442499,0.0007168348,0.0006417704,0.001371061,0.0002019471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008084682,0.000643607,0.2161242,0.0001375017,0.0001632514,0.0003563861,0.0001185788,0.3152707,0.01924398,0.0007650366,0.004619916,0.4417484],"study_design_scores_gemma":[0.000006736571,0.00008441713,0.01745129,0.000007742427,0.00001811726,0.00007679043,0.00002953471,0.9720849,0.009222844,0.0005120683,0.0004902153,0.00001530615],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7987245,0.0005078841,0.1909818,0.0004576302,0.0001775649,0.0001362444,0.001122391,0.004290834,0.003601175],"genre_scores_gemma":[0.9833771,0.00003586751,0.01570297,0.00003195877,0.00002879766,0.00001590782,0.0004095167,0.00004900544,0.0003488669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006488257,"threshold_uncertainty_score":0.01290101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04958445930733132,"score_gpt":0.2790780807297217,"score_spread":0.2294936214223904,"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."}}