{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005567396,0.000172166,0.0002215028,0.0002360477,0.0002782427,0.0004855126,0.0004170171,0.00004974519,0.00001039222],"category_scores_gemma":[0.0002360276,0.0001484166,0.00003483016,0.0007111115,0.0000887597,0.0009731905,0.0002854882,0.0001089185,0.00001659857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000170092,"about_ca_system_score_gemma":0.0000162412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000746743,"about_ca_topic_score_gemma":0.000002049522,"domain_scores_codex":[0.9986507,0.000003284516,0.0001972291,0.00044731,0.0003886808,0.000312823],"domain_scores_gemma":[0.9994312,0.00006145117,0.00007707583,0.0001923434,0.0002053216,0.00003263704],"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":[0.00001061058,0.0001245177,0.1603937,0.0003646885,0.00004062592,0.00003242144,0.000365666,0.4051216,0.0006101068,0.0007098166,0.000274849,0.4319514],"study_design_scores_gemma":[0.0001573531,0.000005906417,0.01437966,0.00009956228,0.00002021941,0.000002373434,0.00001364005,0.9839931,0.00004181858,0.0003921383,0.0006729606,0.0002212326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5208383,0.00004626934,0.4786552,0.00006273181,0.0002026664,0.00002628178,2.66058e-7,0.0001558797,0.00001240211],"genre_scores_gemma":[0.9826416,0.000002974301,0.0158332,0.0008297077,0.0006605259,0.00000763878,0.000007763697,0.00001286666,0.000003760608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5788715,"threshold_uncertainty_score":0.6052254,"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."}}