{"id":"W2092295977","doi":"10.1145/2335484.2335520","title":"Solving manufacturing equipment monitoring through efficient complex event processing","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Complex event processing; Computer science; Event (particle physics); Visualization; Latency (audio); Key (lock); Real-time computing; Throughput; Distributed computing; Data mining; Operating system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004242142,0.0001911641,0.0002484568,0.0001301467,0.000446067,0.0005478289,0.0007882798,0.00004069019,0.0009886887],"category_scores_gemma":[0.0002416519,0.0001371776,0.0001089876,0.0002553612,0.00005647921,0.0009985275,0.0009307556,0.0001185102,0.0006765815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001356942,"about_ca_system_score_gemma":0.00001955878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007008944,"about_ca_topic_score_gemma":0.000004284689,"domain_scores_codex":[0.9961469,0.0001377325,0.0007348,0.0004484478,0.001826343,0.000705779],"domain_scores_gemma":[0.9986141,0.0002875615,0.0002299356,0.0006246103,0.00007148073,0.0001723218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004706224,0.001172847,0.01033543,0.0001662586,0.00008612903,0.00001254054,0.0173041,0.01691146,0.003034074,0.0237541,0.02175951,0.9054165],"study_design_scores_gemma":[0.001280428,0.0001012324,0.1272477,0.0002881905,0.00008013323,0.00001987194,0.04361391,0.02282028,0.1017395,0.008277467,0.6930489,0.001482343],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3707809,0.0004610179,0.5528609,0.001254335,0.001940196,0.000484204,0.000007312511,0.0002349013,0.0719762],"genre_scores_gemma":[0.9797473,0.000007546436,0.01761786,0.000334216,0.000337934,0.00001834678,0.000004349153,0.00001230781,0.001920152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9039341,"threshold_uncertainty_score":0.9999245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3285297737278659,"score_gpt":0.458375936229109,"score_spread":0.1298461625012431,"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."}}