{"id":"W2610379834","doi":"","title":"Hierarchical Channel Recovery for Heterogeneous Cognitive Radio Networks","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Cognitive radio; Channel (broadcasting); Computer science; Heuristic; Reconfigurability; Set (abstract data type); Transmission (telecommunications); Mathematical optimization; Computer network; Artificial intelligence; Telecommunications; Mathematics; Wireless","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005205566,0.000474575,0.0005271753,0.000365527,0.0004857234,0.0007308766,0.0008189661,0.0003453064,0.00002781667],"category_scores_gemma":[0.0002128564,0.0004773962,0.0003888899,0.0006324156,0.0001253614,0.0007711098,0.0003503154,0.0005501013,0.00002660298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003075426,"about_ca_system_score_gemma":0.0001530107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001267696,"about_ca_topic_score_gemma":0.0004975467,"domain_scores_codex":[0.9966664,0.0001942816,0.0005375512,0.0009012388,0.0003228107,0.00137772],"domain_scores_gemma":[0.9974573,0.000776134,0.0002233247,0.0007343588,0.0002923811,0.0005164806],"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":[0.0002871863,0.0005362694,0.001285461,0.00004937927,0.0003843611,0.000243484,0.0005267485,0.03977016,0.001213137,0.03734299,0.01210994,0.9062509],"study_design_scores_gemma":[0.000741297,0.0004320035,0.004095839,0.00009604769,0.0000293168,0.0004045944,0.00002247534,0.9736493,0.00166145,0.01775411,0.0005002352,0.000613353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02462252,0.001563736,0.9669567,0.003331103,0.0004063932,0.001805344,0.00001970452,0.0008467151,0.0004477298],"genre_scores_gemma":[0.9126287,0.0003057245,0.0805629,0.004412008,0.0006590452,0.0009167232,0.00003376453,0.00008050045,0.0004006668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9338791,"threshold_uncertainty_score":0.9997678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050566300949707,"score_gpt":0.2185756821523781,"score_spread":0.2080700191428811,"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."}}