{"id":"W2782704337","doi":"","title":"Supporting Model Refinement with Equivalence Checking in the Context of Model-Driven Engineering with UML-RT","year":2017,"lang":"en","type":"preprint","venue":"Open Archive Toulouse Archive Ouverte (University of Toulouse)","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Computer science; Unified Modeling Language; Equivalence (formal languages); Model checking; Context (archaeology); Process (computing); Programming language; Software engineering; Theoretical computer science; Software","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","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0009123799,0.0007683991,0.001304736,0.0006145493,0.0003713494,0.0002392958,0.01158584,0.0001838236,0.000007023463],"category_scores_gemma":[0.0000289981,0.0006929098,0.0002740681,0.0002469757,0.0004639084,0.0009719224,0.009113048,0.001445534,0.000002367427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002034635,"about_ca_system_score_gemma":0.000855597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003957191,"about_ca_topic_score_gemma":0.003578389,"domain_scores_codex":[0.9958874,0.0001986622,0.0006214188,0.001423057,0.000998116,0.000871334],"domain_scores_gemma":[0.9945799,0.0002157469,0.00136473,0.003406353,0.0002245066,0.000208752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002979994,0.0002674932,0.0008189026,0.0006013733,0.000278304,0.000290457,0.04230118,0.9080784,0.001010595,0.03645884,0.0007039183,0.008892565],"study_design_scores_gemma":[0.001233406,0.0003655237,0.0009174167,0.002495299,0.0001091391,0.00002681192,0.0003527083,0.9903809,0.0002953027,0.002632559,0.0003647261,0.0008262437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03039779,0.00002390382,0.9650568,0.000413975,0.00004851196,0.001869467,0.0003071296,0.0002256765,0.001656759],"genre_scores_gemma":[0.4352215,0.00009567735,0.564357,0.00004803201,0.00001128209,0.00001886895,0.00004556694,0.00005455034,0.0001475226],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4048237,"threshold_uncertainty_score":0.9995522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03371499665657278,"score_gpt":0.2621683266418198,"score_spread":0.228453329985247,"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."}}