{"id":"W2911791687","doi":"10.1109/mwscas.2018.8624045","title":"Multiplatform Spectrum Sensing Prototype","year":2018,"lang":"en","type":"article","venue":"","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; Computer science; Ultra high frequency; Cognitive radio; Usability; Spectrum (functional analysis); Channel (broadcasting); Radio spectrum; Real-time computing; Telecommunications; Wireless; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004414086,0.0009194933,0.0005570661,0.0005199175,0.0003425969,0.0007028081,0.002596579,0.001133689,0.01512923],"category_scores_gemma":[0.0008023408,0.0003493757,0.0004200295,0.000263561,0.000386718,0.001026325,0.001084363,0.0005187517,0.003118576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003164581,"about_ca_system_score_gemma":0.0003197564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001786144,"about_ca_topic_score_gemma":0.001685381,"domain_scores_codex":[0.9991941,0.00006789682,0.00003878593,0.0002706407,0.0003397468,0.00008881803],"domain_scores_gemma":[0.9994569,0.0001346523,0.00004591956,0.0001509464,0.0001487273,0.00006295595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002893806,0.001374412,0.005893511,0.0007761754,0.0001480096,0.001350823,0.0009146614,0.01084858,0.6563187,0.008176643,0.01395055,0.2973541],"study_design_scores_gemma":[0.0006234195,0.004433419,0.01575314,0.0001066685,0.0002217407,0.006194083,0.0005666955,0.330173,0.5452003,0.004769664,0.09164641,0.0003113568],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3223556,0.0003282533,0.6099558,0.0006851536,0.0003410869,0.00226904,0.001849223,0.02564295,0.03657279],"genre_scores_gemma":[0.7007087,0.0001415416,0.2624959,0.0005382281,0.00006517708,0.000746528,0.001002442,0.0002916676,0.03400981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01512923,"threshold_uncertainty_score":0.05061233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630790325768192,"score_gpt":0.2426580809291322,"score_spread":0.2263501776714502,"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."}}