{"id":"W3122406465","doi":"10.1109/tsc.2021.3054036","title":"Identifying a Minimum Sequence of High-Level Changes Between Workflows","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Workflow; Computer science; Scalability; Heuristics; Pruning; Heuristic; Sequence (biology); Theoretical computer science; Algorithm; Artificial intelligence; Database","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000247861,0.00023664,0.0004198341,0.0003808521,0.00038358,0.0002730824,0.0003365387,0.0001022009,0.00007606619],"category_scores_gemma":[0.000004203962,0.0002486058,0.0001473754,0.001579348,0.00003423912,0.0005575715,0.0000151836,0.0002271325,0.00005696296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002226816,"about_ca_system_score_gemma":0.00003088524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001311966,"about_ca_topic_score_gemma":0.0008969711,"domain_scores_codex":[0.9984049,0.00001809003,0.0004227183,0.0004612994,0.0003730096,0.0003199823],"domain_scores_gemma":[0.9987409,0.00009294081,0.0003261096,0.0003222961,0.0004982611,0.00001954263],"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":[0.00007056529,0.0005687797,0.004766228,0.007320622,0.0008718654,0.00007283032,0.001390229,0.3761655,0.02447241,0.0005783416,0.00002925971,0.5836933],"study_design_scores_gemma":[0.001878916,0.00002529259,0.004146777,0.004281508,0.001620767,0.00001265272,0.00369156,0.9453284,0.03294699,0.003738273,0.0008012921,0.001527627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5264255,0.00007758836,0.4722042,0.0005408262,0.0003951248,0.00005505193,0.00001513298,0.0001404583,0.000146071],"genre_scores_gemma":[0.9947566,0.00001968113,0.003728739,0.0007277661,0.0005904762,0.000005179453,0.00003559484,0.0000359938,0.0001000019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5821657,"threshold_uncertainty_score":0.9999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07655935502924761,"score_gpt":0.2754126508831076,"score_spread":0.19885329585386,"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."}}