{"id":"W2128002369","doi":"10.1109/glocom.2006.149","title":"GEN02-1: Hierarchical Iterative Algorithm for a Coupled Constrained OSNR Nash Game","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nash equilibrium; Game theory; Mathematical optimization; Computer science; Iterative method; Best response; Constraint (computer-aided design); Channel (broadcasting); Normal-form game; Algorithm; Mathematics; Repeated game; Mathematical economics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007664209,0.0002035233,0.0002617011,0.00006397872,0.00005035645,0.00006241962,0.000192151,0.0001814681,0.00006771088],"category_scores_gemma":[0.00003844551,0.000197284,0.00009570353,0.0001820417,0.0001677567,0.00007249285,0.00004591688,0.00021793,0.00005377633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007886019,"about_ca_system_score_gemma":0.00001603565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001149929,"about_ca_topic_score_gemma":0.00002273877,"domain_scores_codex":[0.9989412,0.00001028045,0.0002583222,0.0002181814,0.0001169688,0.0004550315],"domain_scores_gemma":[0.9994693,0.0001885214,0.00002130479,0.0002151949,0.00004689705,0.00005882185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007952053,0.0004094077,0.0007826703,0.0002387868,0.0004931756,0.0001910907,0.000290165,0.03764528,0.008218443,0.2588239,0.07805859,0.614769],"study_design_scores_gemma":[0.0008918447,0.0000766099,0.0004643041,0.00002122546,0.0000183169,0.00001598765,0.0000275362,0.9615092,0.0008337405,0.01763372,0.01819894,0.0003086247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.193976,0.000733767,0.7904339,0.0007426552,0.0006778816,0.0009687053,0.0002569442,0.00349168,0.008718416],"genre_scores_gemma":[0.7426497,0.00001631574,0.2564294,0.00007924579,0.0003310133,0.0001316904,0.00007910049,0.00004584292,0.0002376013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9238639,"threshold_uncertainty_score":0.8045008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006190928254146906,"score_gpt":0.2106949013831338,"score_spread":0.2045039731289869,"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."}}