{"id":"W2165761770","doi":"10.1109/glocom.2010.5683960","title":"Autonomous Spectrum Balancing Using Multiple Reference Lines for Digital Subscriber Lines","year":2010,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crosstalk; Computer science; Digital subscriber line; Limiting; Process gain; Distributed computing; Electronic engineering; Spread spectrum; Computer network; Engineering; Code division multiple access","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006631159,0.0001330491,0.0001315433,0.0000631228,0.00008625731,0.00009942125,0.0002343654,0.00007365391,0.0000504992],"category_scores_gemma":[0.00009318483,0.0001212102,0.00005231047,0.00009409842,0.00002257811,0.0002566042,0.00005789772,0.0001788879,0.00002266744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001928648,"about_ca_system_score_gemma":0.00002234803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004445632,"about_ca_topic_score_gemma":0.0005162882,"domain_scores_codex":[0.9993876,0.000002807903,0.0002072417,0.0001252856,0.00004918632,0.0002278407],"domain_scores_gemma":[0.9992904,0.0001212196,0.00002232144,0.0004484908,0.00005235939,0.00006517572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008044115,0.000501485,0.07046106,0.0004063658,0.0002784458,0.000009077456,0.0009776434,0.03802537,0.8007066,0.01633978,0.007553686,0.06466004],"study_design_scores_gemma":[0.000349639,0.00001582813,0.001863156,0.00001816316,0.00001244318,0.00001190565,0.00003521047,0.9057921,0.01902231,0.0004238491,0.07214536,0.0003100905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431039,0.0001321006,0.04918293,0.0001489073,0.000607392,0.000191435,0.0000565132,0.0005464318,0.00603039],"genre_scores_gemma":[0.9698362,0.00001484877,0.02934455,0.00001954229,0.0002205059,0.000016194,0.00004314079,0.00003428426,0.0004707107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8677667,"threshold_uncertainty_score":0.4942808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02816813588922213,"score_gpt":0.2549976144270083,"score_spread":0.2268294785377862,"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."}}