{"id":"W1846534677","doi":"10.7287/peerj.preprints.1459v1","title":"Using machine translation for converting <i>Python</i> <i>2</i> to <i>Python</i> <i>3</i> code","year":2015,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Python (programming language); Programming language; Computer science; Machine translation; Artificial intelligence; Natural language processing","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":[0.006987879,0.001792182,0.0008386903,0.003050634,0.001723426,0.002968628,0.0009857547,0.001021157,0.009793827],"category_scores_gemma":[0.04583452,0.000814504,0.001142908,0.003662294,0.0008531046,0.003902999,0.002545903,0.002810247,0.01090151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008922291,"about_ca_system_score_gemma":0.002901225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003398661,"about_ca_topic_score_gemma":0.005688782,"domain_scores_codex":[0.9894609,0.004996828,0.001224112,0.001776357,0.002041451,0.0005003535],"domain_scores_gemma":[0.9725112,0.01002354,0.001750189,0.00765922,0.007651898,0.0004039865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007750139,0.0006953811,0.0171527,0.00252781,0.0004344788,0.0008322551,0.002589158,0.01390254,0.05098846,0.008721095,0.07454775,0.8268334],"study_design_scores_gemma":[0.0003277551,0.001320133,0.03142143,0.000866014,0.0003901937,0.002781843,0.003091991,0.2626178,0.3625393,0.03759079,0.2963868,0.0006659299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1369857,0.0007063237,0.7086783,0.001753292,0.001577769,0.001602144,0.01560208,0.1115463,0.021548],"genre_scores_gemma":[0.2765453,0.000381601,0.6651757,0.0006071781,0.0001100617,0.00123274,0.03382822,0.01393345,0.008185706],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009793827,"threshold_uncertainty_score":0.03695589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022247769537297,"score_gpt":0.3466878108172054,"score_spread":0.2444630338634757,"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."}}