{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007884589,0.0000654168,0.00008860064,0.00003028263,0.00002438676,0.000005243417,0.00004933987,0.00005077753,0.00002195007],"category_scores_gemma":[0.00001285874,0.00006328167,0.0000199443,0.0002087523,0.000009038169,0.00007902343,0.00001810824,0.00006896169,0.000009668893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001401569,"about_ca_system_score_gemma":0.000001311857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005371088,"about_ca_topic_score_gemma":0.00003132725,"domain_scores_codex":[0.9995526,0.0000106281,0.0001689806,0.00007932241,0.00007162288,0.0001168103],"domain_scores_gemma":[0.9997477,0.00004788949,0.00002246751,0.0001280014,0.000031617,0.00002233264],"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":[0.000003034611,0.000003078691,0.0001349397,0.00004554946,0.00000755589,2.409565e-7,0.00004456197,0.9973746,0.0001098338,0.0003159862,0.0002885876,0.001672038],"study_design_scores_gemma":[0.00009718453,0.0000076891,0.007071036,0.000003446692,0.000004057006,3.69728e-7,0.0000234636,0.9911143,0.0007617486,0.0007821125,0.00007570654,0.00005888883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02200325,0.00002051638,0.9742571,0.00004700256,0.0003957424,0.00006009961,0.00000671182,0.0009935803,0.002215997],"genre_scores_gemma":[0.9964439,0.000045466,0.003191226,0.00004360761,0.00007547693,0.000004835636,0.00007529384,0.00001488411,0.0001052914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9744407,"threshold_uncertainty_score":0.2580552,"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."}}