{"id":"W2061106957","doi":"10.1155/2007/45812","title":"Advanced Signal Processing and Computational Intelligence Techniques for Power Line Communications","year":2007,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Signal processing; Line (geometry); Power (physics); SIGNAL (programming language); Digital signal processing; Power-line communication; Telecommunications; Computer hardware; Mathematics","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":[0.000401638,0.0006261506,0.0006355922,0.0005750147,0.0001990939,0.0008551739,0.0004302574,0.0006297028,0.003306883],"category_scores_gemma":[0.001825596,0.0001812932,0.0003908626,0.001273429,0.0004331038,0.00104959,0.0005615277,0.00155984,0.001143732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001923219,"about_ca_system_score_gemma":0.0003236635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000645001,"about_ca_topic_score_gemma":0.0007017544,"domain_scores_codex":[0.9997295,0.00008356867,0.00002040427,0.00004153889,0.0001098444,0.00001513647],"domain_scores_gemma":[0.9994342,0.0003090136,0.00004272824,0.00007814496,0.0001227906,0.00001299461],"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":[0.00009772611,0.00008369199,0.0003455086,0.0003539292,0.00007480026,0.0001178903,0.00008556497,0.1352277,0.01275781,0.1348997,0.0104035,0.7055522],"study_design_scores_gemma":[0.00001677203,0.00005896922,0.0003215449,0.00003227197,0.00002075177,0.0001131758,0.00002003516,0.8821704,0.002856244,0.1022225,0.0121539,0.00001344784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002335193,0.003626839,0.9907974,0.0005495069,0.0002365601,0.00001252988,0.00004029681,0.0001287798,0.002272823],"genre_scores_gemma":[0.1579372,0.01262213,0.8168387,0.0004153556,0.0013775,0.0001268733,0.0002317398,0.00008746615,0.01036305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003306883,"threshold_uncertainty_score":0.01106268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02607536784492221,"score_gpt":0.3436217252496064,"score_spread":0.3175463574046842,"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."}}