{"id":"W2119278397","doi":"10.1109/icc.2008.993","title":"Sizing Eligible Route Sets for Restorable Network Design and Optimization","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sizing; Set (abstract data type); Path (computing); Network planning and design; Variety (cybernetics); Range (aeronautics); Test set; Linear programming; Mathematical optimization; Distributed computing; Computer network; Algorithm; Artificial intelligence; Engineering; 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":[0.001291633,0.001069211,0.0007388579,0.0009021191,0.0004546681,0.0009083967,0.0008937428,0.0005143451,0.002765249],"category_scores_gemma":[0.003570182,0.0004546413,0.0004610936,0.0006793293,0.0004637648,0.001750574,0.0006685816,0.0008196962,0.0004737895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006256307,"about_ca_system_score_gemma":0.001315693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001166519,"about_ca_topic_score_gemma":0.003887084,"domain_scores_codex":[0.9994127,0.0002517115,0.00002937726,0.00006692584,0.0001584572,0.00008088867],"domain_scores_gemma":[0.9985301,0.0009028566,0.0001776179,0.0001651302,0.0001869933,0.00003719353],"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":[0.0001088312,0.0001212034,0.0005637095,0.0001484259,0.0000317038,0.00009930531,0.0001033333,0.8766164,0.0172595,0.0182483,0.001237436,0.08546177],"study_design_scores_gemma":[0.00002250789,0.0001247701,0.0001584842,0.00001940087,0.00001859961,0.00004948954,0.00006965114,0.9718346,0.009462953,0.01613386,0.002089188,0.00001646386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08769698,0.0002420754,0.9043904,0.0001584175,0.00003330996,0.0001624983,0.00008076552,0.0004751635,0.006760415],"genre_scores_gemma":[0.4566685,0.000296408,0.5402358,0.00007068548,0.00001929708,0.0002519622,0.0001167911,0.0001973523,0.00214321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002765249,"threshold_uncertainty_score":0.009250641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02914132386069989,"score_gpt":0.2394998266192136,"score_spread":0.2103585027585137,"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."}}