{"id":"W2068562910","doi":"10.1002/ett.1533","title":"Optimal distributed interference avoidance: potential game and learning","year":2012,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Potential game; Computer science; Nash equilibrium; Interference (communication); Distributed algorithm; Graph; Mathematical optimization; Channel (broadcasting); Game theory; Selection (genetic algorithm); Wireless network; Wireless; Distributed computing; Theoretical computer science; Artificial intelligence; Mathematics; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001895713,0.0006964881,0.001111911,0.0005002856,0.000514178,0.00109876,0.001589236,0.001077966,0.001374013],"category_scores_gemma":[0.005355617,0.0003293096,0.0004949044,0.0005980759,0.002058597,0.001539584,0.001606015,0.001133412,0.0001650189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449391,"about_ca_system_score_gemma":0.001212741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002721398,"about_ca_topic_score_gemma":0.001540211,"domain_scores_codex":[0.9987092,0.0006673065,0.00003772898,0.0001861297,0.0002235086,0.0001761405],"domain_scores_gemma":[0.9973291,0.001909571,0.0002317418,0.000135239,0.0002248345,0.00016952],"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":[0.00006735904,0.00004582006,0.0002710115,0.00003085139,0.00002568244,0.00005205416,0.00005572726,0.9423572,0.0009339335,0.04371702,0.0003682387,0.01207527],"study_design_scores_gemma":[0.00001374533,0.00002010922,0.00003437508,0.00000295805,0.000002868476,0.000009629047,0.000007590402,0.9813185,0.000169062,0.01828593,0.0001313679,0.000003844838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03443903,0.0001079584,0.9618503,0.0002699763,0.00001738717,0.00004882337,0.00001921995,0.00006802117,0.003179304],"genre_scores_gemma":[0.9401252,0.00009445465,0.05721941,0.00008992389,0.00002190026,0.000131818,0.00003026279,0.00002055772,0.002266538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002721398,"threshold_uncertainty_score":0.01051611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02426587431752859,"score_gpt":0.2759121245108567,"score_spread":0.2516462501933281,"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."}}