{"id":"W4250878855","doi":"10.32920/ryerson.14653566.v1","title":"Efficient techniques for cooperative spectrum sensing in cognitive radio networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; False alarm; Overhead (engineering); Reliability (semiconductor); Fusion center; Dual (grammatical number); Detector; Throughput; Mathematical optimization; Algorithm; Artificial intelligence; Wireless; Mathematics","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":[0.002173661,0.0009148006,0.0008001937,0.0008473461,0.0004696645,0.0007976978,0.001343903,0.0008332204,0.0008314929],"category_scores_gemma":[0.004996971,0.0004970545,0.0006880134,0.0008483393,0.001120003,0.001339257,0.001773383,0.001187036,0.0002518218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006944332,"about_ca_system_score_gemma":0.0008153625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006162176,"about_ca_topic_score_gemma":0.0007381099,"domain_scores_codex":[0.998235,0.0006666005,0.00006045968,0.0001769846,0.0007569207,0.0001040263],"domain_scores_gemma":[0.9984458,0.001013778,0.0001482082,0.0002063216,0.0001647126,0.00002122362],"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":[0.00008791414,0.0000842492,0.0002854237,0.0002514842,0.00008506649,0.00009198765,0.0002317367,0.6645108,0.012251,0.1890726,0.001017082,0.1320306],"study_design_scores_gemma":[0.00001623413,0.00004778298,0.0000644934,0.00001998915,0.00001082335,0.00005677362,0.00001971087,0.9586844,0.001631932,0.03791187,0.001526803,0.000009111082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002740383,0.0003142456,0.9956558,0.00004977876,0.00001455558,0.00002145099,0.000004085591,0.00003355625,0.001166272],"genre_scores_gemma":[0.4821843,0.001434415,0.5132824,0.0001450014,0.0001065834,0.0004010764,0.0000422442,0.00003458907,0.002369546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002173661,"threshold_uncertainty_score":0.01149559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823372715012339,"score_gpt":0.2669804606086368,"score_spread":0.2487467334585134,"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."}}