{"id":"W2142452574","doi":"10.1109/ccece.2006.277287","title":"Modelling and Emulation of Multifractal Noise in Performance Evaluation of Mesh Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Manitoba; University of Winnipeg; University of Manitoba","funders":"","keywords":"Wireless mesh network; Emulation; Mesh networking; Troubleshooting; Computer science; NeuRFon; Wireless; Wireless sensor network; Noise (video); Wireless network; Computer network; Embedded system; IEEE 802.11s; Key distribution in wireless sensor networks; Telecommunications; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0005993832,0.00006987927,0.0001208732,0.0001108616,0.00002081136,0.00001236521,0.0001308628,0.00005715291,0.00000264587],"category_scores_gemma":[0.000005750596,0.00006702458,0.0000183377,0.000303686,0.00002776063,0.0002884728,0.00005005353,0.00005921713,2.206904e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002470709,"about_ca_system_score_gemma":0.00001717233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002575513,"about_ca_topic_score_gemma":0.00004727931,"domain_scores_codex":[0.9990063,0.00006030281,0.0003031166,0.0001769053,0.0003304371,0.0001229886],"domain_scores_gemma":[0.9994262,0.0000712221,0.0001345778,0.0001882157,0.0001646189,0.00001516941],"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":[0.000004535792,0.00003909139,0.0072776,0.000004933047,0.00000128073,1.085587e-7,0.00008497862,0.9701353,0.0002420921,0.002900932,0.000002336673,0.01930679],"study_design_scores_gemma":[0.0003853115,0.000017957,0.01176921,0.00003525793,0.000004279883,6.556221e-7,0.000005004233,0.9855506,0.002067606,0.00009776827,9.187046e-7,0.00006545718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5626034,0.00006296997,0.4367485,0.000007325345,0.00003606608,0.00006412822,6.425816e-8,0.00001093843,0.0004665891],"genre_scores_gemma":[0.9800386,0.00002413678,0.01988879,0.000003901597,0.00002088732,0.000003004722,0.000003148568,0.000004026505,0.00001346149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4174352,"threshold_uncertainty_score":0.2733183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02021713720603983,"score_gpt":0.2366998017898192,"score_spread":0.2164826645837793,"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."}}