{"id":"W3188410362","doi":"10.1109/tse.2021.3101818","title":"Combining Genetic Programming and Model Checking to Generate Environment Assumptions","year":2022,"lang":"en","type":"article","venue":"Aisberg (University of Bergamo)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; H2020 Excellent Science; Fonds National de la Recherche Luxembourg","keywords":"Computer science; Component (thermodynamics); Spurious relationship; Soundness; Flexibility (engineering); Benchmark (surveying); Software; Genetic programming; State (computer science); Artificial intelligence; Machine learning; Algorithm; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001027099,0.0000670772,0.00008713095,0.00008600564,0.0007440471,0.00001770471,0.0003893697,0.00001701028,0.00003627943],"category_scores_gemma":[0.000001059959,0.00009625847,0.00003528863,0.0002292471,0.00005888531,0.0001656298,0.0008494005,0.00008697904,0.00000853928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006328976,"about_ca_system_score_gemma":0.00003152039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001289929,"about_ca_topic_score_gemma":0.00001282729,"domain_scores_codex":[0.9992934,0.00002813793,0.00007666268,0.0002681624,0.0001799779,0.0001535881],"domain_scores_gemma":[0.9995736,0.00001540588,0.00005972408,0.0002468236,0.0000178879,0.00008649211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007439649,0.0003818122,0.002499194,0.00001591306,0.00006721984,0.00002382074,0.009759793,0.7880422,0.004464115,0.08793394,0.001023852,0.1057807],"study_design_scores_gemma":[0.0002714666,0.00008671102,0.01297367,0.000004665184,0.00001806813,0.00001614006,0.0009301927,0.9729636,0.00003959004,0.001330788,0.0111846,0.0001804567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3148122,0.0001001048,0.6829892,0.001732115,0.00002311153,0.0001317647,0.000007049011,0.00004142877,0.0001630729],"genre_scores_gemma":[0.6679708,0.00002632671,0.3313743,0.00004990383,0.000005758212,0.000003726961,0.000004040294,0.000003679659,0.0005614493],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3531586,"threshold_uncertainty_score":0.5722684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419964987583469,"score_gpt":0.189868005760138,"score_spread":0.1756683558843034,"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."}}