{"id":"W4252288227","doi":"10.7287/peerj.preprints.3123","title":"Finding and correcting syntax errors using recurrent neural networks","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Syntax error; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001555143,0.001891769,0.0006824512,0.001571362,0.0004402295,0.001187072,0.002024225,0.001221854,0.001622131],"category_scores_gemma":[0.01351977,0.0007923681,0.000812367,0.0008925853,0.0005842352,0.002465445,0.001144622,0.001723196,0.001177186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174298,"about_ca_system_score_gemma":0.001447588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01121848,"about_ca_topic_score_gemma":0.0169805,"domain_scores_codex":[0.9984377,0.0004103723,0.0001088887,0.0005677611,0.000316654,0.0001586674],"domain_scores_gemma":[0.9945392,0.002575825,0.0008015835,0.000721462,0.001215599,0.0001464122],"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":[0.0005394867,0.0002900815,0.01724138,0.0004535931,0.0002969646,0.001239271,0.001023805,0.1985671,0.06001181,0.003257736,0.01407568,0.703003],"study_design_scores_gemma":[0.00001967641,0.000066121,0.001579537,0.00004337214,0.00006521432,0.0001037431,0.0001097206,0.9766481,0.01580598,0.003842176,0.001686618,0.00002974458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.285657,0.0009843677,0.66222,0.001234348,0.0003413619,0.0001139058,0.001004044,0.04534204,0.003102797],"genre_scores_gemma":[0.7287058,0.0003166632,0.2639612,0.0003265951,0.00005054957,0.00008833617,0.001783268,0.001033983,0.003733546],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01121848,"threshold_uncertainty_score":0.02230638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0793394262160843,"score_gpt":0.3405356839980847,"score_spread":0.2611962577820004,"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."}}