{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003790506,0.0000519798,0.0001064545,0.0001464755,0.00003350942,0.00003278478,0.0001970656,0.00003542664,0.00001210599],"category_scores_gemma":[0.00007399347,0.00004323817,0.0000724483,0.0006644599,0.00001179232,0.0002058588,0.00003403252,0.00003375598,0.00001508424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001310475,"about_ca_system_score_gemma":0.00001209172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003488969,"about_ca_topic_score_gemma":0.00001829538,"domain_scores_codex":[0.999282,0.000009572774,0.000226718,0.0001763226,0.0001766744,0.0001286954],"domain_scores_gemma":[0.9995673,0.0000984076,0.00007165617,0.0001016678,0.0001320453,0.00002890889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002738593,0.00002124771,0.0004137244,0.00001778943,0.00002344487,0.000001230604,0.0004632954,0.0003917359,0.0002173573,0.9760339,0.002561312,0.01985219],"study_design_scores_gemma":[0.00009614727,0.00006072234,0.002224468,0.000005390376,0.000008261462,7.422049e-7,0.00004466477,0.995655,0.001212852,0.00005965335,0.0005884833,0.00004363144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03285782,0.00001507224,0.9651005,0.0004118098,0.0002289723,0.0001403134,0.00000308845,0.0001443617,0.001098113],"genre_scores_gemma":[0.9919602,0.000006766493,0.007454044,0.00009360218,0.0001014376,0.00002428598,0.00001932338,0.000003298464,0.0003370699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9952632,"threshold_uncertainty_score":0.1763202,"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."}}