{"id":"W2015498037","doi":"10.1117/12.570697","title":"Prediction-based dynamic bandwidth allocation in WiFi","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Computer science; Dynamic bandwidth allocation; Autoregressive integrated moving average; Bandwidth (computing); Bandwidth allocation; Queue; Real-time computing; Queueing theory; Computer network; Wireless; Network packet; Telecommunications; Time series; Machine learning","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":[0.00124262,0.0004745934,0.0005817478,0.0003999679,0.00034021,0.0006120346,0.0009301734,0.0006100743,0.0004136659],"category_scores_gemma":[0.005287086,0.0003305077,0.0002149998,0.0005563776,0.0005514696,0.001152841,0.0004279542,0.0006737669,0.00006719495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000815225,"about_ca_system_score_gemma":0.0007076777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01874644,"about_ca_topic_score_gemma":0.009000978,"domain_scores_codex":[0.9995494,0.0001231747,0.00001609463,0.00009016415,0.0001099189,0.0001112654],"domain_scores_gemma":[0.9982919,0.001175767,0.0001815302,0.0000748832,0.0002106659,0.00006527911],"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":[0.0000801477,0.00002953422,0.002385993,0.00001512241,0.00002043488,0.00004072948,0.00003577477,0.9708506,0.001688155,0.002209328,0.0002706355,0.02237349],"study_design_scores_gemma":[0.000002233037,0.00001007229,0.0002634902,7.615407e-7,0.000002557859,0.000005517914,0.000004656895,0.9988972,0.0002093258,0.0005617955,0.00003978066,0.000002594361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3866039,0.0006161521,0.610114,0.0005160117,0.00007603424,0.00002849906,0.00006529057,0.0003477463,0.001632401],"genre_scores_gemma":[0.9889216,0.000127656,0.01050194,0.00001948491,0.00002812581,0.00001007249,0.00002236091,0.000009766747,0.0003589669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01874644,"threshold_uncertainty_score":0.03727466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294148468806087,"score_gpt":0.2476739109243868,"score_spread":0.2347324262363259,"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."}}