{"id":"W2032029859","doi":"10.1109/twc.2014.011714.130685","title":"Wide Band Time-Correlated Model for Wireless Communications under Impulsive Noise within Power Substation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Markov chain; Noise (video); Computer science; Wireless; Impulse noise; Markov process; Noise measurement; Electronic engineering; Telecommunications; Mathematics; Statistics; Engineering; Noise reduction; 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":[0.001036363,0.00100422,0.0009700074,0.0006846824,0.0005840968,0.0009868151,0.001777621,0.001411665,0.003328927],"category_scores_gemma":[0.003556248,0.000453925,0.0008746848,0.0008425279,0.001320724,0.00158655,0.0008157615,0.001544979,0.0005850666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164176,"about_ca_system_score_gemma":0.001038224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01042135,"about_ca_topic_score_gemma":0.005352887,"domain_scores_codex":[0.9990393,0.0002136968,0.00004370926,0.0002586229,0.0002487933,0.0001959089],"domain_scores_gemma":[0.9975615,0.001274733,0.0004510386,0.000160246,0.0004546302,0.0000978793],"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":[0.00006481851,0.00004415069,0.0008994843,0.0000376128,0.00002631475,0.0001800389,0.0001168647,0.9604622,0.002358118,0.03271818,0.000384529,0.00270779],"study_design_scores_gemma":[0.000006958375,0.00001824652,0.0001354522,0.000004117086,0.00001087203,0.00002073215,0.00001069152,0.9960132,0.0002410422,0.003400721,0.0001309018,0.000007076816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06717267,0.0002469945,0.9267659,0.000366494,0.00008680201,0.00007760139,0.0002710498,0.0002849333,0.004727623],"genre_scores_gemma":[0.9652467,0.0005816487,0.02215214,0.0001682311,0.00006997429,0.0002554549,0.0003309905,0.00006808581,0.01112665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01042135,"threshold_uncertainty_score":0.02072144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02152902074953843,"score_gpt":0.2552521588853742,"score_spread":0.2337231381358358,"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."}}