{"id":"W2134750211","doi":"10.1007/978-3-642-00328-8_10","title":"Bayesian Classification of Events for Task Labeling Using Workflow Models","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Research and Productivity Council","funders":"","keywords":"Workflow; Computer science; Task (project management); Data mining; Workflow technology; Bayesian probability; Event (particle physics); Workflow engine; Cluster analysis; Workflow management system; Identification (biology); Identifier; Machine learning; Artificial intelligence; Database; Engineering; Programming language","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":[0.006102109,0.001492294,0.001984957,0.003902024,0.001290491,0.002645167,0.003961073,0.003208331,0.00431368],"category_scores_gemma":[0.01534682,0.001304012,0.002694467,0.002951987,0.0007806858,0.004081069,0.002136685,0.003858969,0.003307284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002330392,"about_ca_system_score_gemma":0.003728616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01573965,"about_ca_topic_score_gemma":0.02430473,"domain_scores_codex":[0.9965084,0.00100595,0.0002848931,0.001012976,0.0007368524,0.0004508575],"domain_scores_gemma":[0.9912457,0.005673241,0.0004544305,0.0009561618,0.001306577,0.0003639239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00162273,0.0009816275,0.006934851,0.0003974107,0.0002595392,0.0002469863,0.0003916373,0.2338625,0.009385085,0.02621522,0.02392186,0.6957806],"study_design_scores_gemma":[0.00002770493,0.00003951992,0.0006346766,0.00004745602,0.00003788051,0.00005005199,0.00003182991,0.9573463,0.002676009,0.03729654,0.001785787,0.00002631294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007988075,0.0002677421,0.9880287,0.0002416022,0.00006178875,0.0001312534,0.0006781521,0.00176574,0.0008369659],"genre_scores_gemma":[0.2805676,0.0006459694,0.7035165,0.0003116571,0.0002114922,0.0007393105,0.007185129,0.0007062628,0.006116259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01573965,"threshold_uncertainty_score":0.03227144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375719708156471,"score_gpt":0.2495225195196495,"score_spread":0.2157653224380848,"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."}}