{"id":"W2742396485","doi":"10.7287/peerj.preprints.3123v1","title":"Finding and correcting syntax errors using recurrent neural networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Syntax error; Syntax; Security token; Abstract syntax; Programming language; Parsing; Artificial intelligence; Natural language processing; Language model; Abstract syntax tree","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":[0.0003362268,0.00009115019,0.00009563988,0.00006759684,0.0005218626,0.0006349272,0.0006865957,0.00003814394,0.000005368422],"category_scores_gemma":[0.0007850733,0.00008463897,0.00002310987,0.00007994779,0.00003755289,0.0004993398,0.0007179228,0.0002168432,0.00000237911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000416783,"about_ca_system_score_gemma":0.00001484592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001141078,"about_ca_topic_score_gemma":0.000009768524,"domain_scores_codex":[0.9991204,0.00002071376,0.00009885745,0.0002726071,0.0001656527,0.0003218124],"domain_scores_gemma":[0.9989488,0.0003414571,0.00005512193,0.0005244671,0.0000310552,0.00009913641],"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":[0.00000413825,0.00001848061,0.4133575,0.00003047272,0.00002056286,0.00007199866,0.0005316459,0.03484049,0.0001863219,0.0009916211,0.0002930048,0.5496538],"study_design_scores_gemma":[0.00007288134,0.00001550837,0.04262958,0.00003257283,0.000001330479,0.00005433616,0.00001159126,0.956904,0.0001343008,0.00001872593,0.00002199575,0.0001031778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5104504,0.00006263738,0.4881262,0.00009206355,0.001002538,0.0000514969,8.183154e-8,0.0001274415,0.00008725147],"genre_scores_gemma":[0.9808038,0.000003870033,0.01900868,0.00001560036,0.00009192318,0.00000208979,1.27146e-7,0.000008177393,0.00006567583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9220635,"threshold_uncertainty_score":0.6122617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06346682147086354,"score_gpt":0.329639314751271,"score_spread":0.2661724932804074,"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."}}