{"id":"W3090644682","doi":"10.3390/telecom1030012","title":"Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters","year":2020,"lang":"en","type":"article","venue":"Telecom","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"White spaces; Ultra high frequency; Computer science; Wireless; Telecommunications; Range (aeronautics); Real-time computing; Cognitive radio; Engineering","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":[0.0001662588,0.0001247428,0.0002039014,0.00005990083,0.00005598493,0.00005995726,0.0002156205,0.00003830498,0.00001492861],"category_scores_gemma":[0.0001081152,0.0001155415,0.00007650288,0.0003671993,0.00004393457,0.00008906621,0.00003200421,0.000134122,0.00001183142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003015207,"about_ca_system_score_gemma":0.00006257407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006114181,"about_ca_topic_score_gemma":4.990584e-7,"domain_scores_codex":[0.9989194,0.0001005734,0.0002198439,0.0003276644,0.0002332184,0.0001992836],"domain_scores_gemma":[0.9992666,0.0002636898,0.0001007969,0.0002163808,0.00005917572,0.0000934088],"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":[0.00008828984,0.000190122,0.00003783129,0.00008336989,0.00002645427,0.00002204413,0.0007688571,0.5880835,0.0005531886,0.003612667,0.0009192618,0.4056144],"study_design_scores_gemma":[0.0003962304,0.0003497759,0.0002707269,0.00002420532,0.000008958998,0.000004455078,0.000009516425,0.9968144,0.001382731,0.0005520961,0.00005841819,0.0001284934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03895324,0.00001833021,0.9569907,0.001302277,0.00004247163,0.0002584042,0.000001872846,0.0001281149,0.002304637],"genre_scores_gemma":[0.8445221,8.660618e-7,0.1543715,0.001054348,0.00003179822,0.000003678915,0.00000573661,0.000007618243,0.00000239205],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8055688,"threshold_uncertainty_score":0.4711645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02692326665176436,"score_gpt":0.2206810290274287,"score_spread":0.1937577623756643,"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."}}