{"id":"W3150752166","doi":"10.1109/tcomm.2021.3070892","title":"Spectrum Sensing for Symmetric α-Stable Noise Model With Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Gaussian noise; Computer science; Noise (video); Robustness (evolution); Impulse noise; Detector; Convolutional neural network; Additive white Gaussian noise; Cognitive radio; Noise measurement; Algorithm; Artificial intelligence; Channel (broadcasting); Noise reduction; Telecommunications; Wireless","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":[0.0001451846,0.000184416,0.0001944444,0.0001746903,0.00109094,0.0002461016,0.0006929133,0.00009274741,0.00001057119],"category_scores_gemma":[0.000007567803,0.0001880891,0.0001489852,0.001508722,0.000106634,0.0003555312,0.00001393713,0.0004239077,0.00000864062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226654,"about_ca_system_score_gemma":0.0001619323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000211948,"about_ca_topic_score_gemma":0.0002312046,"domain_scores_codex":[0.9986436,0.0001046307,0.0002818627,0.0003814865,0.0002222656,0.0003661418],"domain_scores_gemma":[0.9971916,0.0004687132,0.00009348235,0.001760466,0.0003429685,0.0001427423],"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":[0.00001742032,0.0002053414,0.00000115211,0.000003367519,0.00005063325,0.000002338144,0.00002973745,0.9712009,0.00004891244,0.01287425,0.000289442,0.01527645],"study_design_scores_gemma":[0.0005648494,0.00007329557,0.00001694111,0.00001734475,0.00003892269,0.00008743818,0.00002966216,0.9952157,0.0008494495,0.001511419,0.001371516,0.0002234838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001485558,0.0002383168,0.9936786,0.004070542,0.0003951039,0.0002474553,0.00008182192,0.00028234,0.000857308],"genre_scores_gemma":[0.8100985,0.0001606379,0.1883261,0.0004567147,0.00003882289,0.00004992977,0.00003855966,0.00002135816,0.0008093436],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8099499,"threshold_uncertainty_score":0.8390741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02972079299919946,"score_gpt":0.2485622136133014,"score_spread":0.218841420614102,"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."}}