{"id":"W2405556033","doi":"10.1109/saner.2016.110","title":"Pattern Analysis of TXL Programs","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Programmer; Program comprehension; Programming language; Feature (linguistics); Task (project management); Identification (biology); Natural language processing; Source code; Language identification; Artificial intelligence; Software engineering; Natural language; Software; Software system; Linguistics; Engineering","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":[0.0008319193,0.0002791827,0.0002666042,0.002985172,0.0004536585,0.0009829791,0.0004927095,0.0003628835,0.001509196],"category_scores_gemma":[0.007240136,0.0001760042,0.0003663426,0.002062174,0.0005047912,0.000846978,0.0006040302,0.0003438575,0.0003362406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006041762,"about_ca_system_score_gemma":0.0006702013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002341679,"about_ca_topic_score_gemma":0.002840111,"domain_scores_codex":[0.9985644,0.0002554014,0.0001327346,0.0003448944,0.0005812967,0.0001212826],"domain_scores_gemma":[0.9918538,0.003052985,0.001635734,0.001225273,0.002070605,0.0001615653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005659203,0.0002537819,0.2721139,0.001039869,0.0001000794,0.002913729,0.008079271,0.01227574,0.1071015,0.008354644,0.004981364,0.5822201],"study_design_scores_gemma":[0.00009931537,0.0007535981,0.3883435,0.000231339,0.0001600375,0.006655273,0.004638749,0.3856126,0.1644587,0.01447362,0.03444974,0.0001235362],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8082834,0.0002442623,0.1793388,0.0003506883,0.00002243392,0.0002589513,0.002163729,0.004231405,0.00510625],"genre_scores_gemma":[0.8748962,0.0001265775,0.1191495,0.00005171387,0.00001031567,0.000243185,0.002160511,0.00035876,0.003003319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002985172,"threshold_uncertainty_score":0.005048752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108433717708769,"score_gpt":0.2704798483802654,"score_spread":0.2493955112031777,"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."}}