{"id":"W2188814629","doi":"","title":"Learning Model Transformations from Examples using FCA: One for All or All for One?","year":2012,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Executable; Transformation (genetics); Schema (genetic algorithms); Model transformation; Set (abstract data type); Class (philosophy); Theoretical computer science; Formal concept analysis; Artificial intelligence; Data mining; Programming language; Machine learning; Algorithm","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":[0.004108313,0.0009945594,0.001154082,0.001400981,0.0007438397,0.001873805,0.002543817,0.002003875,0.004743302],"category_scores_gemma":[0.01828147,0.0004874041,0.001650214,0.001211452,0.001098859,0.004684203,0.001658354,0.00240323,0.001171131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044525,"about_ca_system_score_gemma":0.001632117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005594109,"about_ca_topic_score_gemma":0.009194249,"domain_scores_codex":[0.9971145,0.001174528,0.0001714019,0.000778403,0.0006019017,0.0001591956],"domain_scores_gemma":[0.9907386,0.006197001,0.0003165909,0.001604857,0.0009487747,0.0001941874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004852795,0.0005698164,0.01145515,0.0004420419,0.0004904965,0.0003357246,0.0003755558,0.09793871,0.00358165,0.01842595,0.01621243,0.8496872],"study_design_scores_gemma":[0.00004138027,0.0000612792,0.0008469617,0.00008627662,0.000121474,0.0001953643,0.0001596712,0.9439207,0.004677039,0.04454811,0.005318622,0.00002319541],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07882246,0.001036523,0.9058582,0.003041702,0.0001108773,0.0002477244,0.0005767753,0.004482751,0.005823068],"genre_scores_gemma":[0.4322378,0.0004027364,0.5615985,0.0006720559,0.00007275876,0.0001820936,0.001973042,0.0003105709,0.002550487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005594109,"threshold_uncertainty_score":0.02172709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2579802686604538,"score_gpt":0.3368610556600236,"score_spread":0.07888078699956985,"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."}}