{"id":"W2086323216","doi":"10.1109/broadnets.2006.4374299","title":"Design and Dimensioning of a Novel composite-star WDM Network with TDM Channel Partitioning","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensioning; Provisioning; Computer science; Network topology; Integer programming; Computer network; Channel (broadcasting); Topology (electrical circuits); Network planning and design; Linear programming; Wavelength-division multiplexing; Star (game theory); Distributed computing; Mathematical optimization; Algorithm; Engineering; Mathematics","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":[0.0002527201,0.0002472383,0.0003299863,0.0002771935,0.0003217754,0.0004616202,0.0005174591,0.0002550835,0.0006117958],"category_scores_gemma":[0.0003765688,0.0001801214,0.0002166783,0.0003132871,0.0003367773,0.0005527968,0.0004158508,0.0002834185,0.0001213693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004755018,"about_ca_system_score_gemma":0.0004302572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005763939,"about_ca_topic_score_gemma":0.001077089,"domain_scores_codex":[0.9998196,0.00004796303,0.000008602677,0.0000459708,0.00005143504,0.0000264541],"domain_scores_gemma":[0.9998121,0.00005122108,0.00003781375,0.00002521083,0.00004798769,0.00002571582],"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":[0.0002901328,0.0001246052,0.001227151,0.0001846674,0.00005533211,0.0002232208,0.0001475375,0.7140815,0.121939,0.04167295,0.001246442,0.1188075],"study_design_scores_gemma":[0.00002139222,0.0001619479,0.0002790214,0.000004836705,0.00001569209,0.0001095383,0.00002448278,0.9757904,0.01584708,0.003979397,0.00375753,0.000008660697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1504754,0.0002119823,0.8436483,0.0001246957,0.00004093505,0.00006392224,0.00007180667,0.0002080978,0.005154981],"genre_scores_gemma":[0.6795057,0.0001253355,0.318768,0.00004028925,0.00001427024,0.00007631657,0.00008719484,0.00002562617,0.001357286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006117958,"threshold_uncertainty_score":0.003449976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01807655602150451,"score_gpt":0.2099603401292219,"score_spread":0.1918837841077174,"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."}}