{"id":"W2795249660","doi":"10.1007/s11276-018-1720-5","title":"Spectrum and energy efficiency of cooperative spectrum prediction in cognitive radio networks","year":2018,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Cognitive radio; Computer science; Efficient energy use; Spectral efficiency; Hidden Markov model; Multilayer perceptron; Energy (signal processing); Artificial neural network; Perceptron; Channel (broadcasting); Artificial intelligence; Algorithm; Telecommunications; Wireless; Statistics; Mathematics","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":[0.0004720931,0.0003672282,0.0005625552,0.0002728633,0.0002679445,0.0001516993,0.0003853532,0.0002313196,0.00003565917],"category_scores_gemma":[0.00002657057,0.0003615861,0.00008675568,0.001532319,0.0005630617,0.0004184617,0.0002694353,0.0004305136,0.000002520889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009727337,"about_ca_system_score_gemma":0.00008053288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001821173,"about_ca_topic_score_gemma":0.001304375,"domain_scores_codex":[0.9971783,0.000253779,0.0005919519,0.0008613117,0.0002957235,0.0008190003],"domain_scores_gemma":[0.9986591,0.000401765,0.0002427981,0.0003732766,0.0001446851,0.000178371],"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":[0.001242858,0.00142744,0.05564059,0.00005947192,0.0005721197,0.0006045588,0.006690893,0.1317644,0.000506742,0.2960762,0.003178391,0.5022364],"study_design_scores_gemma":[0.001172976,0.0006177779,0.01457488,0.0003197638,0.0000208956,0.00008291853,0.0000793697,0.9811136,0.0008057597,0.0007487882,0.0001068459,0.0003563953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1492706,0.001343228,0.8441837,0.0002178631,0.000813176,0.000190663,0.000004491619,0.0001190964,0.003857221],"genre_scores_gemma":[0.9974307,0.000713143,0.0002989695,0.0002013069,0.001153533,0.00001199642,0.00001256533,0.00003014775,0.0001475777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8493493,"threshold_uncertainty_score":0.9998836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008679502226986534,"score_gpt":0.2191921873015131,"score_spread":0.2105126850745266,"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."}}