{"id":"W2481273569","doi":"10.4018/978-1-4666-6571-2.ch012","title":"Dynamic Spectrum Management Algorithms for Multiuser Communication Systems","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in wireless technologies and telecommunication book series","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wireline; Computer science; Offset (computer science); Wireless; Algorithm; Computational complexity theory; Iterative method; Mathematical optimization; Distributed computing; Mathematics; Telecommunications","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":[0.0003337767,0.001050579,0.0006122765,0.0003811565,0.0003756645,0.001283821,0.0009043661,0.000737137,0.007362762],"category_scores_gemma":[0.0009479088,0.0002892527,0.0003669493,0.0008413118,0.0004610966,0.001303236,0.0008087406,0.001426539,0.002794252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005908227,"about_ca_system_score_gemma":0.0005036276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008775293,"about_ca_topic_score_gemma":0.0008014197,"domain_scores_codex":[0.9996883,0.00006546117,0.00001387889,0.00005270217,0.0001580876,0.00002166828],"domain_scores_gemma":[0.9997939,0.0001025788,0.00001301359,0.00002762877,0.00005589859,0.000007004915],"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":[0.00004096825,0.00005442945,0.0001320889,0.000323857,0.00003224177,0.00006565176,0.0001166458,0.2983185,0.003973777,0.2069098,0.02323751,0.4667945],"study_design_scores_gemma":[0.00001669083,0.00003505647,0.0001189934,0.00007047895,0.000008334372,0.0001275521,0.00003032331,0.8453927,0.001497048,0.0933494,0.05933554,0.00001777703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001533438,0.006417311,0.9715567,0.0002715407,0.0002787645,0.00004138115,0.00005611313,0.0003692537,0.0194756],"genre_scores_gemma":[0.160322,0.02095633,0.761804,0.0004410638,0.0006949489,0.0003983581,0.0005559432,0.0003817711,0.05444551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007362762,"threshold_uncertainty_score":0.02463084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494672202047335,"score_gpt":0.2573472352405652,"score_spread":0.2424005132200918,"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."}}