{"id":"W4391093215","doi":"10.1109/bigdata59044.2023.10386241","title":"AGV Quality of Service Throughput Prediction via Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Silesian University of Technology","keywords":"Throughput; Quality of service; Computer science; Artificial neural network; Telecommunications link; Bandwidth (computing); Mean squared error; Relation (database); Service (business); Computer network; Real-time computing; Distributed computing; Artificial intelligence; Data mining; Telecommunications; Wireless; Mathematics","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":[0.000566893,0.0007799028,0.0004791113,0.0005971822,0.0003019719,0.0006897884,0.0005447883,0.0007114124,0.0006302233],"category_scores_gemma":[0.001777706,0.0003084695,0.0003878161,0.0005023348,0.0002616863,0.0005528648,0.0003914725,0.0008256437,0.0001628517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383424,"about_ca_system_score_gemma":0.0007057351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03478051,"about_ca_topic_score_gemma":0.01806501,"domain_scores_codex":[0.9997573,0.00004307372,0.0000121388,0.00006824126,0.00006466279,0.00005447957],"domain_scores_gemma":[0.9993575,0.0003086977,0.00008947287,0.00002306517,0.0001976945,0.00002360611],"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":[0.0000411269,0.00003138015,0.001331927,0.000007493672,0.000009577418,0.00001401653,0.000005648014,0.9881962,0.0004312901,0.0001197598,0.0001419557,0.009669574],"study_design_scores_gemma":[4.703627e-7,0.000003403967,0.0001380779,4.144714e-7,7.324704e-7,4.834283e-7,6.847746e-7,0.9997041,0.0001055556,0.00003766319,0.000007672335,6.993962e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5885359,0.000464829,0.4033813,0.0004048484,0.0001239887,0.00006416094,0.0003462357,0.001378366,0.005300367],"genre_scores_gemma":[0.9926883,0.00005521233,0.006318226,0.00001652455,0.000007858529,0.00002337828,0.0001124773,0.000008237394,0.0007698488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03478051,"threshold_uncertainty_score":0.06915611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02797663464116692,"score_gpt":0.2607008881551456,"score_spread":0.2327242535139787,"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."}}