{"id":"W3090289035","doi":"10.1145/3365438.3410964","title":"<i>mel</i> - model extractor language for extracting facts from models","year":2020,"lang":"en","type":"article","venue":"","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Extractor; Code (set theory); Programming language; Software; Software engineering; Code generation; KPI-driven code analysis; Base (topology); Artificial intelligence; Natural language processing; Software development; Software construction; Engineering; Operating system; Key (lock)","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":[0.0001352027,0.0002067619,0.000224586,0.00004031385,0.00005723098,0.0001427198,0.0009359709,0.000103845,0.00001134878],"category_scores_gemma":[0.00002399778,0.0002003711,0.000106669,0.0001283436,0.00001033083,0.001147814,0.0002290968,0.0001803778,0.00001332707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000339599,"about_ca_system_score_gemma":0.00005009603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000049832,"about_ca_topic_score_gemma":0.000001957211,"domain_scores_codex":[0.9986376,0.00001753836,0.0002470752,0.0005419448,0.0002350202,0.0003208202],"domain_scores_gemma":[0.9989716,0.0001862301,0.000070464,0.0005081039,0.00006005634,0.0002034955],"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":[0.00002789725,0.00007345511,0.00002832601,0.00008151616,0.00004905214,0.00002656633,0.007150653,0.3408667,0.09786266,0.4603308,0.005869544,0.08763287],"study_design_scores_gemma":[0.0001782072,0.00002954057,0.000007280014,0.00001576279,0.000005051666,0.000001125595,0.000007016109,0.9655786,0.02553912,0.00594915,0.002440371,0.0002487388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005084903,0.00008701962,0.9904662,0.000567723,0.00007689846,0.0003311764,0.00004117308,0.002689362,0.0006555434],"genre_scores_gemma":[0.4080834,0.00000336777,0.5911216,0.0006052324,0.00005799908,0.00003795595,0.000007425063,0.000021351,0.00006164301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6247119,"threshold_uncertainty_score":0.8170897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05637311964648813,"score_gpt":0.2619832851241646,"score_spread":0.2056101654776765,"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."}}