{"id":"W2476369785","doi":"","title":"Achieving Fast Fault Detection and Localization in All-Optical Networks","year":2005,"lang":"en","type":"article","venue":"Optical Fiber Communication Conference","topic":"Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; Carleton University","funders":"","keywords":"Fault detection and isolation; Computer science; Network packet; Fault (geology); Real-time computing; Computer network; Artificial intelligence; Seismology; Geology","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":[0.0006782393,0.0008285839,0.0006909829,0.000881878,0.0007934293,0.0009105153,0.001113952,0.0009411321,0.0007007943],"category_scores_gemma":[0.002593971,0.0003259861,0.0002421412,0.0004375385,0.0006981842,0.003059412,0.001155971,0.000917849,0.0002366233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004712862,"about_ca_system_score_gemma":0.0005932242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008629461,"about_ca_topic_score_gemma":0.001254699,"domain_scores_codex":[0.9994034,0.000118549,0.00004213236,0.0001000351,0.0002322291,0.000103733],"domain_scores_gemma":[0.9981558,0.0007953403,0.000259405,0.0002519506,0.0004789192,0.0000586471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006877307,0.0002434751,0.003184447,0.0006297241,0.0001024201,0.0005430013,0.0004814344,0.1555254,0.1429392,0.02839932,0.003883611,0.6633801],"study_design_scores_gemma":[0.00005883784,0.00047368,0.001207447,0.00006405397,0.00007065768,0.0005214865,0.0001135224,0.8053325,0.1573374,0.02598237,0.008763189,0.00007481952],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03099141,0.001293974,0.9651704,0.0003210791,0.0001236712,0.00005752752,0.00002184365,0.001226056,0.0007939664],"genre_scores_gemma":[0.8224725,0.00137134,0.1739461,0.0001986171,0.0001494743,0.00006310349,0.00004777432,0.00004345971,0.001707593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001113952,"threshold_uncertainty_score":0.003586948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962355430400975,"score_gpt":0.2422283701199484,"score_spread":0.2226048158159387,"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."}}