{"id":"W2022675432","doi":"10.1002/smr.539","title":"TIDIER: an identifier splitting approach using speech recognition techniques","year":2011,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Software Engineering Research","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Identifier; Computer science; Program comprehension; Source code; Maintainability; Unique identifier; Set (abstract data type); Documentation; Software; Code (set theory); Relation (database); Natural language processing; Comprehension; Information retrieval; Artificial intelligence; Data mining; Software engineering; Programming language; Software system","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":[0.001243487,0.0009640281,0.0009395642,0.002890241,0.0004815313,0.001250124,0.0009074048,0.0007089279,0.004289206],"category_scores_gemma":[0.003890217,0.0002980212,0.000742906,0.001459204,0.0004603053,0.001613345,0.001484514,0.0008926347,0.003946458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003624829,"about_ca_system_score_gemma":0.0007629472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328935,"about_ca_topic_score_gemma":0.001416833,"domain_scores_codex":[0.9985335,0.0002933006,0.0001349545,0.0005768767,0.0003848751,0.00007648124],"domain_scores_gemma":[0.9977958,0.0008138782,0.0003034285,0.0003430883,0.0006404937,0.0001032738],"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":[0.0005517608,0.0001250415,0.003182843,0.0002964004,0.0000762169,0.0002979042,0.000751559,0.003957422,0.1081574,0.001975976,0.003813614,0.8768139],"study_design_scores_gemma":[0.0002308855,0.0008379838,0.01360662,0.000115935,0.0003883938,0.002209749,0.001790675,0.6501424,0.2669902,0.0111167,0.05235671,0.0002136613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07439049,0.0003442178,0.9126622,0.0001991959,0.0001798267,0.0002521027,0.0008170512,0.008545562,0.002609313],"genre_scores_gemma":[0.1671216,0.00018077,0.8252863,0.0001112747,0.00006082331,0.0002345705,0.002181039,0.0004921328,0.004331487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004289206,"threshold_uncertainty_score":0.01434886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06451466082664108,"score_gpt":0.2980101579526624,"score_spread":0.2334954971260213,"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."}}