{"id":"W2154288485","doi":"10.1186/1687-1499-2013-173","title":"User pairing in cooperative wireless network coding with network performance optimization","year":2013,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairing; Computer science; Linear network coding; Wireless network; Transmitter power output; Computer network; Wireless; Mathematical optimization; Network performance; Heuristic; Transmission (telecommunications); Optimization problem; Algorithm; Telecommunications; Channel (broadcasting); Mathematics; Network packet; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001288714,0.0003631228,0.0004392562,0.0001902712,0.002171436,0.001052575,0.001952724,0.0001064732,0.00002544383],"category_scores_gemma":[0.00001226448,0.0003090699,0.00005809604,0.001577873,0.0001889849,0.001446282,0.0009034739,0.001140521,0.0000104465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001528142,"about_ca_system_score_gemma":0.00009861351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007360976,"about_ca_topic_score_gemma":0.0001119043,"domain_scores_codex":[0.9969188,0.0009140869,0.0007296771,0.000404515,0.000316534,0.0007164171],"domain_scores_gemma":[0.9967749,0.0007633529,0.0004393738,0.001429111,0.0003732525,0.0002199828],"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":[0.00003162578,0.0001101235,0.03233521,0.0000107561,0.00006252657,0.000008098958,0.0008190571,0.6979029,0.00002835937,0.01956429,0.0008746166,0.2482524],"study_design_scores_gemma":[0.0007376794,0.0002041976,0.007310082,0.001321365,0.00000969822,0.00009927676,0.00007797723,0.9838876,0.000009335115,0.0001001936,0.005815579,0.0004270778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.209146,0.01084941,0.7591632,0.007850316,0.0009078739,0.001465581,8.347235e-7,0.0003832006,0.01023359],"genre_scores_gemma":[0.9246578,0.04516419,0.02869699,0.0008783328,0.0003778348,0.00009008822,0.000007814507,0.00003892085,0.00008808933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7304662,"threshold_uncertainty_score":0.9999844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235820625293343,"score_gpt":0.2551280299944703,"score_spread":0.2227698237415368,"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."}}