{"id":"W2096689971","doi":"10.1109/ccece.2005.1557288","title":"Optimal spectrum management with group power constraint for digital subscriber loops","year":2006,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digital subscriber line; Constraint (computer-aided design); Computer science; Power (physics); Electric power transmission; Transmission (telecommunications); Computer network; Asymmetric digital subscriber line; Electronic engineering; Distributed computing; Telecommunications; Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005432796,0.0004603223,0.0007000463,0.0002597455,0.0003870018,0.000589533,0.0005823075,0.000400689,0.000649319],"category_scores_gemma":[0.001088759,0.0002472578,0.0002380226,0.0003575413,0.000649476,0.0007395226,0.0006573372,0.0004320889,0.0001205036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005011932,"about_ca_system_score_gemma":0.0006308146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001207263,"about_ca_topic_score_gemma":0.00124521,"domain_scores_codex":[0.9992785,0.0002229042,0.00001835858,0.0001290282,0.0002237448,0.0001275674],"domain_scores_gemma":[0.9995872,0.0001933325,0.00008438167,0.00004384118,0.0000618087,0.00002942507],"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":[0.0001929319,0.00007522875,0.000479578,0.00005006896,0.0000345949,0.00008375534,0.000130946,0.861711,0.01516858,0.02515572,0.0009175979,0.09600002],"study_design_scores_gemma":[0.00002430013,0.00004655488,0.00008535066,0.000002084828,0.000005642494,0.00001969473,0.00001012941,0.9904026,0.001613354,0.007370723,0.0004139627,0.000005764338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03979694,0.0001987857,0.9582154,0.00008983811,0.00001516062,0.00001236064,0.00001030008,0.00009907823,0.001562263],"genre_scores_gemma":[0.930666,0.000093972,0.06829311,0.00003848098,0.00003467391,0.00002410941,0.00001525266,0.00001821178,0.0008161872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001207263,"threshold_uncertainty_score":0.003636479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00471237201125265,"score_gpt":0.1814812589429605,"score_spread":0.1767688869317078,"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."}}