{"id":"W4214698899","doi":"10.3390/s22051818","title":"CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Major Scientific and Technological Innovation Project of Shandong Province; Texas Space Grant Consortium","keywords":"Computer network; Computer science; Active queue management; Network congestion; Wireless network; Packet loss; Router; Network packet; Bottleneck; Wireless; Distributed computing; Telecommunications; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007357162,0.0007623018,0.0005909593,0.0005982179,0.0006036015,0.0007437203,0.001947473,0.000656375,0.001479072],"category_scores_gemma":[0.001481069,0.0002080738,0.0004161901,0.000496289,0.0006052037,0.0007316961,0.0007775801,0.001090924,0.0002097979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008048072,"about_ca_system_score_gemma":0.001082061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008618904,"about_ca_topic_score_gemma":0.003807099,"domain_scores_codex":[0.9994491,0.00009846513,0.00003152758,0.0001205763,0.0002191123,0.00008116617],"domain_scores_gemma":[0.9994875,0.000142532,0.00006794884,0.00003481386,0.000223052,0.00004414201],"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":[0.0004044624,0.0002996887,0.001822229,0.0003296635,0.0001184741,0.0003329496,0.0002821634,0.6860182,0.03265923,0.02004509,0.006160041,0.2515278],"study_design_scores_gemma":[0.0000166107,0.00007258447,0.00007714044,0.000004039959,0.000007430437,0.00002236351,0.000004771416,0.9970207,0.001432701,0.0007335488,0.000599738,0.000008275595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01448175,0.000597669,0.9814586,0.0001320281,0.0001846415,0.00008419745,0.00001732473,0.001101286,0.001942516],"genre_scores_gemma":[0.9148213,0.0005024592,0.08135235,0.0001745426,0.00008742647,0.000205306,0.00006919519,0.000050067,0.002737278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008618904,"threshold_uncertainty_score":0.01713747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191034262731812,"score_gpt":0.2291608023460379,"score_spread":0.2100573760728567,"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."}}