{"id":"W4281786414","doi":"10.1002/rnc.6214","title":"Adaptive fractional order predictive sliding mode control for congestion control of wireless access networks","year":2022,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Network congestion; Computer science; Robustness (evolution); Network packet; Queue; Wireless network; Active queue management; Nonlinear system; Fading; Sliding mode control; Model predictive control; Adaptive control; Controller (irrigation); Wireless; Computer network; Control (management); Telecommunications; Channel (broadcasting)","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":[],"consensus_categories":[],"category_scores_codex":[0.0009608069,0.0001822271,0.000462782,0.0002223003,0.0001716587,0.0001634007,0.0009883196,0.00008039184,0.00002640009],"category_scores_gemma":[0.0001061936,0.000165494,0.0001943084,0.0001657725,0.00005536214,0.0008454599,0.0001281562,0.0004432367,2.00921e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113474,"about_ca_system_score_gemma":0.0002534634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002253717,"about_ca_topic_score_gemma":0.000005488351,"domain_scores_codex":[0.9978771,0.0002335379,0.0007025321,0.0002450096,0.0007100038,0.0002317708],"domain_scores_gemma":[0.9957726,0.0011339,0.001086744,0.0001244823,0.001769464,0.000112783],"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":[0.005180675,0.0001936256,0.002461636,0.000006587442,0.0007022666,0.00002457669,0.00009880523,0.971322,0.0001543192,0.006444956,0.0003707616,0.0130398],"study_design_scores_gemma":[0.01042784,0.0009089307,0.0009654085,0.00006530498,0.00007471229,0.0001256225,0.00004528424,0.9842905,0.00002660664,0.0008209067,0.002098078,0.0001507495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004011519,0.000305318,0.9905848,0.001928428,0.0013168,0.00154325,0.0002584798,0.00001732655,0.00003406311],"genre_scores_gemma":[0.9921589,0.00003805513,0.005443893,0.0006272126,0.001263536,0.0004206166,0.00001051432,0.00001616246,0.00002106313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9881474,"threshold_uncertainty_score":0.6748648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02090565556297109,"score_gpt":0.2898039422724976,"score_spread":0.2688982867095265,"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."}}