{"id":"W2056107761","doi":"10.1109/icc.2004.1312441","title":"Optimal multiuser spectrum management for digital subscriber lines","year":2004,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Digital subscriber line; Computer science; Spectrum management; Computer network; Telecommunications; Cognitive radio; Wireless","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":[0.0007767558,0.0005350413,0.00084144,0.0003634182,0.0004962854,0.001061331,0.0007151213,0.0005758629,0.0009243724],"category_scores_gemma":[0.00152005,0.0003744772,0.0002856094,0.0004192544,0.0008455624,0.0009175744,0.001018806,0.0005617228,0.0002384205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101212,"about_ca_system_score_gemma":0.001217715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002188652,"about_ca_topic_score_gemma":0.00280028,"domain_scores_codex":[0.9992261,0.0002936734,0.00002184083,0.000131315,0.0001984683,0.0001286879],"domain_scores_gemma":[0.9993938,0.0002939461,0.0001105828,0.00005033694,0.00009860778,0.00005273621],"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":[0.0001526013,0.00007136735,0.0003817865,0.000039391,0.00002285458,0.00005477334,0.00007625789,0.9142749,0.006490472,0.01168062,0.0009822394,0.06577265],"study_design_scores_gemma":[0.0000143774,0.00002452447,0.00006486627,0.000001718435,0.000003066221,0.00001023639,0.00001081411,0.9937046,0.0009844919,0.004983508,0.0001932995,0.000004383564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04807975,0.0003123417,0.9490659,0.0002019116,0.00002195802,0.00002245731,0.00002438374,0.000243508,0.002027801],"genre_scores_gemma":[0.9042338,0.0001202132,0.0938361,0.00005884263,0.00003457518,0.00003591151,0.00003264101,0.00003149265,0.001616476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002188652,"threshold_uncertainty_score":0.007343471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253449292534536,"score_gpt":0.2281346094950995,"score_spread":0.2156001165697542,"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."}}