{"id":"W2110508548","doi":"10.1186/1687-1499-2012-155","title":"Improving throughput and fairness by improved channel assignment using topology control based on power control for multi-radio multi-channel wireless mesh networks","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer network; Wireless mesh network; Throughput; Network topology; Channel (broadcasting); Interference (communication); Topology control; Power control; Channel allocation schemes; Default gateway; Wireless network; Topology (electrical circuits); Network performance; Distributed computing; Wireless; Power (physics); Telecommunications; Key distribution in wireless sensor networks","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002187889,0.0005738642,0.0007895249,0.0001768997,0.001948048,0.0005392596,0.001444345,0.0003258658,0.000002200257],"category_scores_gemma":[0.00003282362,0.0005239316,0.0001947868,0.0002979996,0.0002910859,0.0006784264,0.0003839025,0.001194183,6.593614e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002432908,"about_ca_system_score_gemma":0.00007903007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003451986,"about_ca_topic_score_gemma":0.00002439828,"domain_scores_codex":[0.9960405,0.0009236292,0.0008854621,0.0006093683,0.000298607,0.001242416],"domain_scores_gemma":[0.9951289,0.001771997,0.0009632785,0.00140106,0.0002174291,0.000517286],"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":[0.002487555,0.008666365,0.0189907,0.0002548862,0.002221085,0.00006503369,0.008146291,0.2527761,0.008601958,0.03002253,0.001286743,0.6664807],"study_design_scores_gemma":[0.006220884,0.0004366325,0.0004679207,0.0002795019,0.00008252735,0.00009082919,0.0001260745,0.9910861,0.00003227767,0.00004146807,0.0005769705,0.000558838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005684478,0.006024082,0.9845752,0.0009806304,0.001445765,0.001154185,0.00001400097,0.0001040746,0.00001754814],"genre_scores_gemma":[0.9800363,0.001252309,0.01643346,0.001482399,0.0005395527,0.0001427908,0.00001103393,0.0000842571,0.00001789084],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9743518,"threshold_uncertainty_score":0.9997212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0388050032192451,"score_gpt":0.2845119465491724,"score_spread":0.2457069433299273,"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."}}