{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001172361,0.000992027,0.001217351,0.0009673748,0.0005457025,0.001569867,0.002526101,0.001574328,0.001684125],"category_scores_gemma":[0.004692084,0.0007878874,0.001082346,0.001062779,0.0009878883,0.002117547,0.001108772,0.001729377,0.0006244474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009747801,"about_ca_system_score_gemma":0.001128885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009894364,"about_ca_topic_score_gemma":0.005913838,"domain_scores_codex":[0.99901,0.0001941776,0.00005063614,0.0003017269,0.0002925648,0.0001509179],"domain_scores_gemma":[0.9979647,0.001131984,0.0003207408,0.0001513543,0.0003851753,0.00004602312],"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":[0.00004414619,0.00002079884,0.001352008,0.00005181828,0.00002360016,0.00009774319,0.00005495889,0.9752617,0.001674827,0.01158453,0.0003941948,0.009439685],"study_design_scores_gemma":[0.000002287751,0.000008380401,0.0001918442,0.000003542203,0.000006766325,0.00002759888,0.000006956899,0.9969782,0.0002542095,0.002301653,0.000211467,0.000007127965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01778036,0.0001662694,0.9797609,0.0001498043,0.00003044318,0.00003116684,0.0002067656,0.0002237644,0.001650549],"genre_scores_gemma":[0.9233617,0.0007329187,0.06781948,0.0001120205,0.0001232713,0.0002509047,0.0006782812,0.0000854602,0.006836047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009894364,"threshold_uncertainty_score":0.01967359,"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."}}