{"id":"W1985281299","doi":"10.1109/broadnets.2008.4769065","title":"Survivable traffic grooming for scheduled demands","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traffic grooming; Computer science; Computer network; Network topology; Distributed computing; Integer programming; Path protection; Routing (electronic design automation); Linear programming; Set (abstract data type); Throughput; Path (computing); Wavelength-division multiplexing; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005357981,0.000573275,0.0004319555,0.0003372495,0.0004272242,0.0007479938,0.0007207103,0.0004683642,0.002382791],"category_scores_gemma":[0.001371421,0.0002851178,0.0005077167,0.0005101103,0.0004663995,0.0009633853,0.0004991952,0.0008889596,0.0001946098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007240912,"about_ca_system_score_gemma":0.001202747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003490826,"about_ca_topic_score_gemma":0.00647386,"domain_scores_codex":[0.999597,0.0001324997,0.000010072,0.00006124858,0.0001122303,0.00008689809],"domain_scores_gemma":[0.9994504,0.0002840898,0.00009418174,0.00005157796,0.00008752488,0.00003222766],"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":[0.0000459936,0.00006906558,0.0002531444,0.0001257808,0.00002380416,0.0001642722,0.00007397032,0.8797432,0.007128967,0.08192915,0.002591301,0.02785144],"study_design_scores_gemma":[0.000004734542,0.00002178108,0.00006329706,0.000007032603,0.000005103404,0.00002395578,0.00001921206,0.9820373,0.0007386874,0.01533145,0.001743957,0.000003360622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01987664,0.0002268603,0.9721236,0.0002203304,0.00004795134,0.00006416501,0.0001010443,0.0001451347,0.007194239],"genre_scores_gemma":[0.7975276,0.0009029218,0.1931066,0.0001951249,0.00009259653,0.0001883849,0.0003268526,0.0001023159,0.00755774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003490826,"threshold_uncertainty_score":0.007971227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192826818611258,"score_gpt":0.2163690304560027,"score_spread":0.1970863485948769,"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."}}