{"id":"W2053493264","doi":"10.1109/icsc.2010.24","title":"Using Model Transformation Semantics for Aspect Composition","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Model transformation; Transformation (genetics); Unified Modeling Language; Context (archaeology); Sequence diagram; Semantics (computer science); Set (abstract data type); Programming language; Composition (language); Aspect-oriented programming; Sequence (biology); Theoretical computer science; Algorithm; Artificial intelligence; Linguistics","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":[0.0002147267,0.00006358883,0.00007250421,0.00005019376,0.00006830356,0.00004092313,0.0002070541,0.00004357609,6.915187e-7],"category_scores_gemma":[0.00004960326,0.00005920329,0.00003307585,0.00008066029,0.00001205688,0.0005626745,0.00002138963,0.00008345667,0.000001219259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001402666,"about_ca_system_score_gemma":0.00001635828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.997481e-7,"about_ca_topic_score_gemma":0.000001389514,"domain_scores_codex":[0.9995791,0.000008899405,0.0001027917,0.0001128903,0.00007293055,0.0001233298],"domain_scores_gemma":[0.9995589,0.0001576397,0.00002371889,0.0001841458,0.00005239122,0.00002322911],"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":[0.000001356867,0.000005779415,0.000001439575,0.00001143199,0.000001972843,1.246744e-7,0.0001885157,0.6532664,0.1232016,0.2193967,0.000006787626,0.003917845],"study_design_scores_gemma":[0.00009242927,0.00001027928,0.00001046033,0.000002460601,0.000002285931,0.00000679365,0.00000359081,0.8704992,0.05738108,0.07189228,0.00002571825,0.00007345531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01161098,0.000002952706,0.9871333,0.000186435,0.0003226071,0.0001408013,0.000001367672,0.0004563158,0.000145184],"genre_scores_gemma":[0.2845977,6.73858e-7,0.7153298,0.00003433109,0.00001838418,0.000004424872,0.000001330487,0.000004139916,0.000009264902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2729867,"threshold_uncertainty_score":0.241424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022566148294036,"score_gpt":0.3510957159710464,"score_spread":0.2488391011416428,"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."}}