{"id":"W1981307456","doi":"10.1109/icc.2010.5502372","title":"Cross-Layer Mixed Bias Scheduling for Wireless Mesh Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Queue; Wireless mesh network; Scheduling (production processes); Wireless network; Node (physics); Computer network; Queueing theory; Distributed computing; Wireless; Mathematical optimization; Telecommunications; Mathematics; 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":[0.001940977,0.0005125121,0.000504771,0.0004273228,0.000422717,0.0006455148,0.001164921,0.0004084712,0.0008552118],"category_scores_gemma":[0.003319754,0.0002941091,0.0002778142,0.0005136181,0.000387504,0.001097282,0.001065772,0.0006891431,0.0002164087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005610454,"about_ca_system_score_gemma":0.0006001767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005848567,"about_ca_topic_score_gemma":0.001068888,"domain_scores_codex":[0.9988744,0.0005181913,0.00004475702,0.0001183029,0.0003401043,0.000104234],"domain_scores_gemma":[0.9983951,0.0007720485,0.0002034326,0.0002293959,0.0003099455,0.00009005616],"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":[0.001207951,0.0002829458,0.006603211,0.0002340244,0.0002465786,0.0001472199,0.0002351599,0.4934363,0.06419472,0.03196655,0.00256069,0.3988846],"study_design_scores_gemma":[0.00003894565,0.0002104438,0.000599887,0.000008620169,0.00002555234,0.00005674892,0.00001797622,0.9840907,0.007124138,0.006267012,0.001544755,0.00001521686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04923648,0.0008804114,0.9480891,0.0001468558,0.0001138913,0.00004171311,0.00002092518,0.0003274835,0.001143172],"genre_scores_gemma":[0.8319489,0.0003169241,0.1657729,0.0002101195,0.0001561106,0.00007796667,0.00006615551,0.00008517931,0.001365754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001940977,"threshold_uncertainty_score":0.01026499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056406324624629,"score_gpt":0.2845634386042725,"score_spread":0.2539993753580262,"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."}}