{"id":"W4313043402","doi":"10.1109/lwc.2022.3217675","title":"BNET: A Neural Network Approach for LLR-Based Detection in the Presence of Bursty Impulsive Noise","year":2022,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"","keywords":"Computer science; Noise (video); Artificial neural network; Algorithm; Wireless; Function (biology); Artificial intelligence; Telecommunications","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":[0.001177832,0.00086575,0.0009671984,0.0008566697,0.0004152562,0.00100178,0.002035391,0.001330326,0.00227408],"category_scores_gemma":[0.004133568,0.0003635807,0.0004094646,0.0009848633,0.0005588564,0.001431685,0.001194351,0.001785729,0.00088304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006940005,"about_ca_system_score_gemma":0.0008972787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004400712,"about_ca_topic_score_gemma":0.005371165,"domain_scores_codex":[0.9993415,0.0001880093,0.00003869905,0.0001186075,0.0002398777,0.00007311295],"domain_scores_gemma":[0.9988906,0.0005794771,0.0001010723,0.00007812451,0.0003114008,0.00003940572],"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":[0.0002915124,0.0001249928,0.0006312265,0.0001445586,0.00008293751,0.0001366707,0.0000471051,0.6538388,0.007713876,0.02007782,0.00401515,0.3128954],"study_design_scores_gemma":[0.000002926927,0.00001428708,0.00003198288,0.000004708557,0.00000340805,0.00001941442,0.000002076422,0.9969687,0.0007722781,0.001777681,0.0003986469,0.000004003856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002513756,0.000221976,0.995537,0.0001010669,0.00006142708,0.00002043059,0.00004591834,0.0004741193,0.001024319],"genre_scores_gemma":[0.3640773,0.001146047,0.6196231,0.0005410127,0.0003485644,0.0002389774,0.0004875002,0.000246502,0.01329099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004400712,"threshold_uncertainty_score":0.0087502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232906318146207,"score_gpt":0.2439594959264192,"score_spread":0.2216304327449571,"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."}}