{"id":"W2804100647","doi":"10.5539/cis.v11n3p1","title":"A Bayesian Network Based Method for Service Quality Optimization","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Context (archaeology); Bayesian network; Service (business); Quality (philosophy); Set (abstract data type); Service quality; Quality assurance; The Internet; Quality of service; Quality of experience; Bayesian probability; Video quality; Fuzzy logic; Data mining; Machine learning; Artificial intelligence; World Wide Web; Computer network; Metric (unit); Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002265648,0.001430141,0.00146847,0.001779799,0.0006383312,0.001036374,0.001691425,0.001404232,0.003319011],"category_scores_gemma":[0.005113831,0.000745045,0.001166175,0.001555683,0.0007098243,0.001703752,0.001133256,0.001605066,0.0004165687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179477,"about_ca_system_score_gemma":0.002331454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01698702,"about_ca_topic_score_gemma":0.01208885,"domain_scores_codex":[0.9986044,0.0005843124,0.00005182518,0.0002542778,0.000396773,0.0001085123],"domain_scores_gemma":[0.9985586,0.0008800083,0.000115782,0.00004517028,0.0003457964,0.000054671],"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":[0.00007819971,0.00004769633,0.0006244627,0.00008993489,0.00007119764,0.00003457073,0.00004542014,0.8994778,0.001412333,0.01599619,0.001424379,0.08069787],"study_design_scores_gemma":[0.000005733966,0.000008861828,0.00006046594,0.0000051639,0.000008227835,0.000007633976,0.000003031578,0.9968989,0.0001420089,0.002509773,0.0003449495,0.000005260995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001533515,0.0001881418,0.9973344,0.0000695109,0.0000174402,0.00003142678,0.00003049604,0.00009081559,0.0007043559],"genre_scores_gemma":[0.3091848,0.001087862,0.6835223,0.0002374289,0.0001727433,0.0005069125,0.0004939316,0.000153752,0.004640207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01698702,"threshold_uncertainty_score":0.03377628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03547636515820929,"score_gpt":0.3669422895748985,"score_spread":0.3314659244166892,"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."}}