{"id":"W2804500867","doi":"10.48550/arxiv.1805.10700","title":"Real-Time Spectrum Sniffer for Cognitive Radio Based on Rotman Lens Spectrum Decomposer","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bandwidth (computing); Cognitive radio; Extremely high frequency; Decomposer; Computer science; Lens (geology); Electronic engineering; Telecommunications; Optics; Engineering; Wireless; Physics","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":[0.0002884433,0.0004637254,0.000291457,0.0003952578,0.0001715853,0.0005960421,0.0006164082,0.0003456649,0.002166318],"category_scores_gemma":[0.0003405124,0.0001345356,0.0002875762,0.0002536367,0.0003528861,0.0006249141,0.0004364879,0.0003644328,0.0006174307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003197445,"about_ca_system_score_gemma":0.0001739469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002309168,"about_ca_topic_score_gemma":0.0003853828,"domain_scores_codex":[0.9997658,0.00004232063,0.00001293632,0.00005573626,0.00009106233,0.00003212842],"domain_scores_gemma":[0.9998341,0.00003715292,0.0000382834,0.00002675019,0.00004108388,0.00002267157],"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":[0.0006543513,0.0001350497,0.001419117,0.0002198044,0.00005542224,0.0006481918,0.0002118324,0.01062398,0.6751932,0.0524804,0.00257361,0.2557851],"study_design_scores_gemma":[0.0001407629,0.001777234,0.002099201,0.00007103035,0.00009535717,0.003244289,0.0001570337,0.3365651,0.5898359,0.01067372,0.05518626,0.0001542239],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09499128,0.001007808,0.888338,0.0002095294,0.0002145504,0.0001049581,0.00007410005,0.0009966918,0.01406306],"genre_scores_gemma":[0.7496482,0.0005011673,0.2422581,0.0002495362,0.00009962906,0.00007912306,0.00007536062,0.00007349746,0.007015318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002166318,"threshold_uncertainty_score":0.00724709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04421248901410748,"score_gpt":0.1851126392786987,"score_spread":0.1409001502645912,"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."}}