{"id":"W4402833805","doi":"10.1109/ojcoms.2024.3467385","title":"An LLR-Based Receiver for Mitigating Bursty Impulsive Noise With Unknown Distributions","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Noise (video); Computer science; Environmental science; Physics; Statistical physics; Artificial intelligence","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":[0.001085195,0.0007935797,0.0007679822,0.0004986039,0.0003025632,0.0007124388,0.001010574,0.001000774,0.001195851],"category_scores_gemma":[0.00406317,0.0003576008,0.0005408053,0.0005312716,0.0005283074,0.001102989,0.0007958976,0.001139318,0.0008914775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000413256,"about_ca_system_score_gemma":0.0009326076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000668279,"about_ca_topic_score_gemma":0.0009101575,"domain_scores_codex":[0.998955,0.0003057131,0.00005442609,0.0001799709,0.0004477795,0.00005715938],"domain_scores_gemma":[0.998852,0.000612438,0.0001529702,0.0001150434,0.0002352511,0.000032259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005572977,0.0002082906,0.001495897,0.0003870295,0.0001562544,0.0003495691,0.0003281156,0.4006264,0.129506,0.0321832,0.003230759,0.4309711],"study_design_scores_gemma":[0.00002050961,0.0001221187,0.0001366909,0.00001430048,0.00003131652,0.0002002069,0.00000879755,0.9799953,0.01566696,0.002102196,0.001680512,0.00002102105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002775901,0.0001440908,0.9961254,0.00005632424,0.00002420609,0.0000142225,0.00001055035,0.0003055944,0.0005437402],"genre_scores_gemma":[0.3158571,0.0006447802,0.6765916,0.0003458284,0.0002127133,0.000133028,0.0001532589,0.0001317475,0.005929905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001195851,"threshold_uncertainty_score":0.005739093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02938901171366765,"score_gpt":0.3161572378382916,"score_spread":0.286768226124624,"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."}}