{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00422093,0.001345563,0.000826084,0.001442086,0.001213443,0.003627032,0.001331068,0.001461026,0.003712323],"category_scores_gemma":[0.006886986,0.0007595515,0.002490992,0.001217093,0.002747855,0.006642088,0.003236057,0.002903891,0.001559461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092871,"about_ca_system_score_gemma":0.001954058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001611825,"about_ca_topic_score_gemma":0.001454932,"domain_scores_codex":[0.9942709,0.001970442,0.0006207002,0.0007980733,0.002053885,0.0002858835],"domain_scores_gemma":[0.9970355,0.00106351,0.0002933903,0.0009126265,0.000599596,0.00009538106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007366834,0.0001103635,0.0003528893,0.0002748901,0.00008946735,0.0004078482,0.0009971561,0.02205177,0.007978668,0.8858221,0.002956503,0.07888483],"study_design_scores_gemma":[0.00008640673,0.0001092688,0.0001368709,0.0001877364,0.0001228447,0.0006800779,0.0002522903,0.1616926,0.02535942,0.6705789,0.1407265,0.00006707885],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001032213,0.00005636118,0.9955503,0.0001407717,0.0000540314,0.00005212687,0.00002819593,0.000902626,0.002183371],"genre_scores_gemma":[0.09781998,0.0004738341,0.8957134,0.0003427106,0.0001367194,0.000456564,0.0004650985,0.001223163,0.003368562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00422093,"threshold_uncertainty_score":0.02232271,"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."}}