{"id":"W2980214767","doi":"10.3390/app9204232","title":"An Accurate Probabilistic Model for TVWS Identification","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"White spaces; Computer science; Cognitive radio; Ultra high frequency; False alarm; Telecommunications; Probabilistic logic; Real-time computing; Computer network; Wireless; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007076986,0.00009076593,0.0001058135,0.00006845761,0.0002905477,0.0004826642,0.0007165605,0.00002926483,0.000003440128],"category_scores_gemma":[0.00001447541,0.00007671841,0.00003075578,0.0004089577,0.00009740912,0.0005719003,0.00004881034,0.00004760997,0.00003679817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002234976,"about_ca_system_score_gemma":0.00009006024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002603818,"about_ca_topic_score_gemma":0.00001305415,"domain_scores_codex":[0.998766,0.00001483914,0.0001698323,0.0005459639,0.0002272825,0.000276097],"domain_scores_gemma":[0.9993594,0.0001112944,0.00009084187,0.0003216064,0.00005842981,0.00005840656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005735959,0.00003889795,0.00006835802,0.000009738885,0.000003134039,2.106111e-7,0.0006534871,0.1125736,0.02367101,0.8250496,0.0001298985,0.03779624],"study_design_scores_gemma":[0.0001089677,0.00004157667,0.0003443941,0.000004115662,0.000002451771,0.000001506585,0.00004172886,0.9190356,0.001007878,0.07923677,0.00005713053,0.0001178992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1626463,0.00001081813,0.8324814,0.000234435,0.0001796641,0.0004656435,0.000001288576,0.00009149365,0.003888934],"genre_scores_gemma":[0.9690427,0.000001999342,0.03056406,0.0001857026,0.00004944144,0.00002746695,0.000002159881,0.000004289426,0.00012222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8064619,"threshold_uncertainty_score":0.4654342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03232664231855815,"score_gpt":0.2828793391176872,"score_spread":0.2505526967991291,"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."}}