{"id":"W2102682822","doi":"10.1109/glocom.2005.1577355","title":"Margin maximization in multiuser interference digital subscriber line channels","year":2005,"lang":"en","type":"article","venue":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Digital subscriber line; Margin (machine learning); Computer science; Maximization; Mathematical optimization; Algorithm; Computer network; 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":[0.001065694,0.0006517299,0.0007697776,0.0002588145,0.0002400471,0.0007327055,0.0005941797,0.0006181605,0.001004207],"category_scores_gemma":[0.003030954,0.0003766908,0.0003192967,0.0003707952,0.001008381,0.0009658171,0.0008413574,0.0006864458,0.0003632319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007402915,"about_ca_system_score_gemma":0.0006629902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002440403,"about_ca_topic_score_gemma":0.00142012,"domain_scores_codex":[0.9994312,0.0002595795,0.00001994159,0.00009455504,0.0001332588,0.00006153236],"domain_scores_gemma":[0.9988343,0.0008178907,0.00009936011,0.00005350526,0.0001620856,0.00003293184],"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":[0.00006694514,0.00002013469,0.0002315669,0.00005521144,0.00001317867,0.00003941049,0.00008106219,0.9588755,0.002643701,0.01423943,0.0005185226,0.02321535],"study_design_scores_gemma":[0.000005167443,0.00001012115,0.00003848537,0.000001905912,0.000001112579,0.000004517615,0.000004192744,0.9962794,0.0005955547,0.00290604,0.000151285,0.000002085424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01392371,0.0001651556,0.9844512,0.00009076473,0.00001168767,0.00001687364,0.0000163442,0.0001088698,0.001215414],"genre_scores_gemma":[0.8204978,0.0003523852,0.1747447,0.00009850536,0.00003656609,0.0001048426,0.00008012402,0.0001124961,0.003972576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002440403,"threshold_uncertainty_score":0.005636036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02658985573717016,"score_gpt":0.2601374769404113,"score_spread":0.2335476212032412,"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."}}