{"id":"W1511484581","doi":"10.1007/978-3-642-12029-9_12","title":"Prescriptive Semantics for Big-Step Modelling Languages","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Semantic compression; Semantics (computer science); Semantic computing; Natural language processing; Programming language; Semantic equivalence; Formal semantics (linguistics); Artificial intelligence; Semantic technology; Semantic Web","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":[0.003538949,0.0009851317,0.0007534174,0.001025792,0.000940848,0.003766457,0.002394087,0.001385552,0.00433628],"category_scores_gemma":[0.006203359,0.001171093,0.001526286,0.001031715,0.003253371,0.005829746,0.002816727,0.004572145,0.001140453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001452413,"about_ca_system_score_gemma":0.00143797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110384,"about_ca_topic_score_gemma":0.002121563,"domain_scores_codex":[0.9973199,0.0009582622,0.0003250977,0.000269635,0.0009755235,0.0001517176],"domain_scores_gemma":[0.9952701,0.002589552,0.0002151551,0.001137228,0.0006316238,0.0001564243],"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":[0.00002581915,0.00001619993,0.00006310287,0.00009209863,0.000009489134,0.00004522374,0.0003352124,0.006892618,0.001063664,0.9777976,0.0006736862,0.01298526],"study_design_scores_gemma":[0.00001990077,0.0000163186,0.00003083385,0.00006160508,0.00001580542,0.00006760783,0.00006890275,0.04164261,0.002676909,0.9381461,0.01723496,0.00001834965],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002603706,0.0001459886,0.9904916,0.0002174515,0.00004772495,0.00004928961,0.0001174432,0.0007600244,0.00556682],"genre_scores_gemma":[0.249594,0.0006026459,0.7350555,0.0004330514,0.0001063478,0.0004231709,0.0007106056,0.0009738695,0.01210077],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00433628,"threshold_uncertainty_score":0.01871598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218439320823735,"score_gpt":0.248130030303062,"score_spread":0.2262860982206885,"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."}}