{"id":"W2516324260","doi":"10.1109/tcomm.2016.2602342","title":"Approximation of Achievable Rates in Additive Gaussian Mixture Noise Channels","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Entropy (arrow of time); Gaussian noise; Gaussian; Amplitude; Upper and lower bounds; Differential entropy; Piecewise; Binary entropy function; Control theory (sociology); Algorithm; Principle of maximum entropy; Mathematical analysis; Computer science; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003596788,0.002171182,0.001520891,0.002076731,0.0005786678,0.002026079,0.001884134,0.001218714,0.002482494],"category_scores_gemma":[0.01556644,0.0006688391,0.001266158,0.001413442,0.002194639,0.002914627,0.001791938,0.002112942,0.0009633299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002208565,"about_ca_system_score_gemma":0.001206603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002485621,"about_ca_topic_score_gemma":0.001566035,"domain_scores_codex":[0.9971061,0.001089073,0.0001074895,0.0002473058,0.001133229,0.0003168089],"domain_scores_gemma":[0.9941772,0.004292096,0.0003715613,0.0005245042,0.0005565594,0.00007811403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000662926,0.00001872006,0.0002870149,0.0001111744,0.00002820865,0.00009721203,0.0001073762,0.8664379,0.002011996,0.11864,0.0003628643,0.01183123],"study_design_scores_gemma":[0.000002061765,0.000007685674,0.00003596963,0.00001875348,0.000004492798,0.00003788983,0.000009385843,0.9800611,0.001008204,0.01848009,0.0003253047,0.000009116211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006609601,0.0003940509,0.9890774,0.00006964591,0.00001702012,0.00002430289,0.00004338003,0.0002346894,0.003529867],"genre_scores_gemma":[0.6518138,0.002273413,0.3391032,0.000153717,0.00009053767,0.000336775,0.0002969822,0.0003562789,0.005575303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003596788,"threshold_uncertainty_score":0.01902181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205449356752734,"score_gpt":0.2573660085841491,"score_spread":0.2368210729088757,"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."}}