{"id":"W2009029954","doi":"10.1007/s00165-009-0114-y","title":"Automating the transformation-based analysis of visual languages","year":2009,"lang":"en","type":"article","venue":"Formal Aspects of Computing","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"Petri net; Graph rewriting; Computer science; Transformation (genetics); Model transformation; Programming language; Semantics (computer science); Operational semantics; Graph; Stochastic Petri net; Visual language; Program transformation; Theoretical computer science; Visual modeling; Theory of computation; Unified Modeling Language; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000465894,0.0001095978,0.0002560451,0.0003023556,0.00008463622,0.00003385766,0.000624201,0.00003658938,0.000002793108],"category_scores_gemma":[0.00001217497,0.00008461025,0.0001637974,0.001052021,0.00002415816,0.0003328076,0.00005377556,0.0001037794,5.297919e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002061654,"about_ca_system_score_gemma":0.00003532438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002052733,"about_ca_topic_score_gemma":0.000002537625,"domain_scores_codex":[0.9989198,0.00003173051,0.0004084042,0.000123414,0.000305436,0.0002111502],"domain_scores_gemma":[0.9992092,0.0001370771,0.0002288686,0.0002955188,0.0001007237,0.00002865115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002987622,0.00006873687,0.0004619732,0.00004699367,0.0001214234,0.000001977721,0.00324786,0.3389764,0.001083461,0.4098359,0.000006233202,0.246146],"study_design_scores_gemma":[0.00009254009,0.000115412,0.02789969,0.00004762971,0.00004435456,0.000001270809,0.00001424452,0.9524238,0.01900057,0.0002562071,0.00001739485,0.00008690147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.221822,0.00002600429,0.7769725,0.0001080858,0.00002038693,0.00008452662,0.000001266216,0.0003339658,0.0006312187],"genre_scores_gemma":[0.7751377,3.491597e-7,0.2247901,0.00005066412,0.00001342982,7.131783e-7,0.00000344966,0.000003024862,6.367115e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6134474,"threshold_uncertainty_score":0.3450306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00614976838597775,"score_gpt":0.262775836309977,"score_spread":0.2566260679239992,"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."}}