{"id":"W2084183558","doi":"10.1109/ciss.2010.5464924","title":"Comprehensive node selection and power allocation in multi-source cooperative mesh networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Relay; Computer science; Selection (genetic algorithm); Resource allocation; Mathematical optimization; Node (physics); Upper and lower bounds; Transmission (telecommunications); Power (physics); Computer network; Mathematics; Telecommunications; Engineering","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":[0.001279289,0.0004402112,0.0005931047,0.0004004274,0.0003392162,0.0005038516,0.0006806811,0.0005160858,0.000428286],"category_scores_gemma":[0.002343209,0.0002814453,0.0002446136,0.000737606,0.0005531426,0.001072472,0.0007619936,0.0002684015,0.00008815929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006286546,"about_ca_system_score_gemma":0.0003941054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008109749,"about_ca_topic_score_gemma":0.001277536,"domain_scores_codex":[0.9993135,0.0003775594,0.0000215507,0.00008112234,0.0001495578,0.00005681591],"domain_scores_gemma":[0.9993175,0.0004651204,0.00006074267,0.00007211678,0.00006269422,0.000021784],"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":[0.00004851035,0.00002736559,0.0004502395,0.00004153942,0.00001891848,0.00006740277,0.00006619673,0.9490406,0.003491376,0.01501564,0.0003656374,0.0313666],"study_design_scores_gemma":[0.000008499895,0.00004824772,0.0002172478,0.000004023334,0.000007657755,0.00002307245,0.00002267177,0.9843418,0.00116873,0.01363808,0.0005151824,0.000004773746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07346308,0.0006918606,0.9230081,0.0001676329,0.0000112849,0.00002832887,0.00002538993,0.00009624354,0.002507987],"genre_scores_gemma":[0.9579439,0.000357408,0.04046581,0.00003240569,0.00002381806,0.00005984707,0.00002813981,0.00001368831,0.001075006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001279289,"threshold_uncertainty_score":0.006765664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03097657517216305,"score_gpt":0.2842867174157925,"score_spread":0.2533101422436294,"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."}}