{"id":"W4213432281","doi":"10.1109/wsc52266.2021.9715373","title":"A Queueing Model for Video Analytics Applications of Smart Cities","year":2021,"lang":"en","type":"article","venue":"2021 Winter Simulation Conference (WSC)","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Computer science; Queueing theory; Cloud computing; Analytics; Scheduling (production processes); Real-time computing; Enhanced Data Rates for GSM Evolution; Distributed computing; Set (abstract data type); Mathematical optimization; Operating system; Database; Computer network; Artificial intelligence","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":[0.001035716,0.000759237,0.000854668,0.0005629795,0.0007648709,0.001578247,0.001711092,0.001416062,0.003535048],"category_scores_gemma":[0.002582547,0.0004011604,0.0009098268,0.0007680883,0.0007007666,0.002002946,0.0007178245,0.001142515,0.0006340502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002887833,"about_ca_system_score_gemma":0.002700124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03530471,"about_ca_topic_score_gemma":0.01303638,"domain_scores_codex":[0.9992539,0.0002217368,0.0000371286,0.0001551786,0.0001871629,0.0001448793],"domain_scores_gemma":[0.9992657,0.000376453,0.00007739493,0.00003885724,0.0001933989,0.00004801703],"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":[0.00003235467,0.00004857058,0.0004911849,0.00003968591,0.00001577651,0.0001113785,0.00009144066,0.9256948,0.001928015,0.0675794,0.0009255198,0.003041888],"study_design_scores_gemma":[0.000005365682,0.000007325203,0.0000518157,0.000001914394,0.000003224108,0.000006188149,0.000009660955,0.9958075,0.0001087312,0.003574051,0.0004205514,0.000003709585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05375548,0.0005448626,0.9314732,0.0009745721,0.0001911375,0.0001708842,0.0004762024,0.0004610279,0.01195264],"genre_scores_gemma":[0.9004964,0.001050922,0.07533636,0.0003253774,0.0001978578,0.0004005871,0.0006000749,0.0001315004,0.02146088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03530471,"threshold_uncertainty_score":0.07019842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04760270942400276,"score_gpt":0.2910909327173837,"score_spread":0.243488223293381,"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."}}