{"id":"W2085592895","doi":"10.1109/tpwrd.2006.883018","title":"Effective Communication Strategies for Noise-Limited Power-Line Channels","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Direct-sequence spread spectrum; Multipath propagation; Electronic engineering; Narrowband; Additive white Gaussian noise; Power-line communication; Gaussian noise; Spread spectrum; Multipath interference; Fading; Computer science; Noise (video); Noise power; Engineering; Channel (broadcasting); Telecommunications; Power (physics); Code division multiple access; Physics; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004161238,0.0002867065,0.0002513463,0.0003596012,0.0003110404,0.00007972968,0.0004226812,0.0001810686,0.00009081557],"category_scores_gemma":[0.000006663489,0.0003089969,0.0002056668,0.0003975707,0.00008513327,0.0004304702,0.000003464966,0.0004160668,0.00007394417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000156746,"about_ca_system_score_gemma":0.00003386394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002923058,"about_ca_topic_score_gemma":0.00009318748,"domain_scores_codex":[0.9987595,0.00005327028,0.0004032751,0.000228524,0.0001626281,0.0003927925],"domain_scores_gemma":[0.9980303,0.0006259163,0.00005620785,0.0009498459,0.0002116162,0.000126121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001652842,0.002434911,0.00001554385,0.0002776068,0.001478083,0.00002710946,0.008809334,0.7682544,0.1150187,0.003841725,0.006945974,0.09124374],"study_design_scores_gemma":[0.0132985,0.004821245,0.002895949,0.001091506,0.0008931792,0.0001742967,0.009542263,0.2597711,0.5489312,0.005382274,0.1477272,0.005471258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1571933,0.0008040118,0.8351706,0.0001152642,0.0009277441,0.00075854,0.0001119206,0.0005806438,0.004338019],"genre_scores_gemma":[0.9959759,0.0003959748,0.00295448,0.0001401289,0.00002659447,0.0002204415,0.00003100924,0.00007458166,0.0001808976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8387826,"threshold_uncertainty_score":0.9999362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548226056088915,"score_gpt":0.2520928537949346,"score_spread":0.2366105932340454,"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."}}