{"id":"W1002013509","doi":"10.1007/978-3-319-18356-5_9","title":"An Improved Machine Learning Approach for Selecting a Polyhedral Model Transformation","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Process (computing); Transformation (genetics); Feature selection; Classifier (UML); Selection (genetic algorithm); Machine learning; Key (lock); Pattern recognition (psychology); 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":[0.0009869594,0.001612125,0.002087177,0.001830592,0.0007764051,0.001354034,0.002276101,0.001711831,0.00901442],"category_scores_gemma":[0.003102206,0.0008462814,0.001570032,0.001739595,0.00062553,0.001283949,0.001586665,0.002511284,0.003819678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005947092,"about_ca_system_score_gemma":0.001129101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00559417,"about_ca_topic_score_gemma":0.006516038,"domain_scores_codex":[0.9989406,0.000171684,0.00006490535,0.0002951377,0.0004561537,0.00007140318],"domain_scores_gemma":[0.999006,0.0003881278,0.00006302851,0.0001868472,0.0003258279,0.00003019613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001344101,0.0001500612,0.0003880735,0.0001872689,0.00005362628,0.00009364905,0.00004979673,0.3644134,0.009616735,0.009851214,0.006185566,0.6088762],"study_design_scores_gemma":[0.0000075505,0.00001831336,0.00005134291,0.000006801432,0.000007701808,0.00002237169,0.000006847798,0.9954414,0.001053941,0.002135046,0.00124323,0.000005407546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001232055,0.00005344102,0.9970863,0.00002632493,0.00002726703,0.00004233605,0.000048977,0.0006930582,0.0007902603],"genre_scores_gemma":[0.0646617,0.0001251737,0.9306446,0.0001121518,0.00005901978,0.0002375369,0.0006171758,0.0004425514,0.003100168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00901442,"threshold_uncertainty_score":0.03015625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02989183017670963,"score_gpt":0.275123016727724,"score_spread":0.2452311865510144,"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."}}