{"id":"W2974587188","doi":"10.1109/icton.2019.8840391","title":"Machine Learning for Regenerator Placement Based on the Features of the Optical Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Regenerative heat exchanger; Software deployment; Computer science; Resource (disambiguation); Resource allocation; Successor cardinal; Quality (philosophy); Network planning and design; Artificial intelligence; Simulation; Machine learning; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187629,0.0008128889,0.0006945004,0.0008124243,0.0004127068,0.0007001318,0.0007390646,0.0007636473,0.001058819],"category_scores_gemma":[0.006326175,0.000295435,0.0003909805,0.0006219391,0.0003443566,0.001041981,0.0003413892,0.001154977,0.0003144713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007888452,"about_ca_system_score_gemma":0.0007534486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004749296,"about_ca_topic_score_gemma":0.005964582,"domain_scores_codex":[0.9997368,0.00007056587,0.00002193693,0.00007138747,0.00005465836,0.00004458448],"domain_scores_gemma":[0.9968606,0.002319513,0.0002534952,0.0001304039,0.0003816935,0.00005425659],"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":[0.0001033524,0.0001125569,0.005570342,0.00003724225,0.00002329646,0.00003959445,0.00001926594,0.9041249,0.0008963709,0.001017443,0.001191297,0.08686435],"study_design_scores_gemma":[0.00000245527,0.00001063714,0.0002594908,0.000002384352,0.000001745834,0.000004392913,0.000002480346,0.9988771,0.0002258347,0.0005653586,0.0000464536,0.000001674603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3214875,0.001030403,0.6703409,0.001511721,0.000121498,0.0001487234,0.0006001236,0.00131934,0.003439781],"genre_scores_gemma":[0.9070224,0.0003388368,0.09028468,0.0001216821,0.00008347086,0.0001068292,0.0005347656,0.00003668816,0.001470763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004749296,"threshold_uncertainty_score":0.009443283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007192909626300538,"score_gpt":0.2047861517532171,"score_spread":0.1975932421269166,"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."}}