{"id":"W4230715589","doi":"10.1109/isspit.2018.8642738","title":"Real-Time Multi-Channel TVWS Sensing Prototype Using Software Defined Radio","year":2018,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"White spaces; Computer science; Channel (broadcasting); Cognitive radio; Software-defined radio; Software; Wireless; Real-time computing; Occupancy; Telecommunications; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0003240847,0.0002604852,0.000294279,0.0001519678,0.000468139,0.0002851843,0.0003197367,0.0001036848,0.00004267615],"category_scores_gemma":[0.0000943802,0.0002377422,0.0001059391,0.000594152,0.0001242515,0.0004222714,0.0002316905,0.0001501049,0.0001787218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140564,"about_ca_system_score_gemma":0.0001225275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003206055,"about_ca_topic_score_gemma":0.00008360259,"domain_scores_codex":[0.9980792,0.0001071893,0.0003003216,0.0006308693,0.0002609486,0.0006214413],"domain_scores_gemma":[0.9987461,0.0001324785,0.0001138763,0.0005472533,0.0002893102,0.0001709955],"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":[0.0005976708,0.0009781428,0.001720459,0.0001861424,0.0007416125,0.001773686,0.01379852,0.005137799,0.2190363,0.01451162,0.01059013,0.7309279],"study_design_scores_gemma":[0.0005264221,0.0001670137,0.0005138207,0.00008718769,0.00001319582,0.0002415613,0.00001614957,0.9925168,0.004380743,0.0008567804,0.0002945037,0.0003858021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04670189,0.00002474524,0.9491515,0.0001724096,0.0004131442,0.0004857681,0.00000101493,0.0006633793,0.00238614],"genre_scores_gemma":[0.3426743,0.000009458789,0.656014,0.0002248544,0.0005550884,0.000001484008,0.000002121511,0.00003471066,0.0004839859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.987379,"threshold_uncertainty_score":0.9694844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02949300447356016,"score_gpt":0.2611747198500121,"score_spread":0.2316817153764519,"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."}}