{"id":"W2203597119","doi":"","title":"Mining common morphological fragments from process event logs","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Process mining; Code refactoring; Process (computing); Event (particle physics); Data mining; Business process discovery; Process modeling; Work in process; Software engineering; Artificial intelligence; Business process; Business process management; Business process modeling; Programming language; Software; Engineering","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":[0.002012051,0.001432792,0.001015171,0.009000612,0.001137278,0.002406104,0.001704444,0.001343871,0.001608635],"category_scores_gemma":[0.01452528,0.0007158329,0.00153236,0.006258982,0.0008479636,0.002673901,0.001844549,0.001291819,0.001058894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007093671,"about_ca_system_score_gemma":0.001950877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005042396,"about_ca_topic_score_gemma":0.00490182,"domain_scores_codex":[0.9967739,0.0004021567,0.0004272143,0.0006396143,0.001513439,0.0002437085],"domain_scores_gemma":[0.9868837,0.006267984,0.002420114,0.001551108,0.002548051,0.0003290004],"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":[0.001387668,0.0009271667,0.06524296,0.001587347,0.0002797534,0.008840453,0.004007364,0.05315349,0.06363269,0.01742754,0.009080893,0.7744327],"study_design_scores_gemma":[0.0001458594,0.0005037459,0.05869868,0.0004429562,0.0003725283,0.005845438,0.003947423,0.7583539,0.06485305,0.06341545,0.04318244,0.0002384604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1917497,0.00080907,0.7916874,0.0004022602,0.00007687535,0.0008644296,0.00577493,0.006062457,0.002572931],"genre_scores_gemma":[0.4409706,0.0006762609,0.5369781,0.00009204864,0.00007363898,0.0007459992,0.01817735,0.0004405372,0.0018455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009000612,"threshold_uncertainty_score":0.01064086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096041938625573,"score_gpt":0.2123986259174516,"score_spread":0.2014382065311958,"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."}}