{"id":"W2146648240","doi":"10.1109/icpc.2009.5090025","title":"Automatic classication of large changes into maintenance categories","year":2009,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Waterloo","funders":"","keywords":"Commit; Computer science; Metadata; Categorization; Software maintenance; Task (project management); Programming language; Information retrieval; Software; Artificial intelligence; Database; Software system; World Wide Web; 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":[0.002146271,0.0008368041,0.0007425899,0.007089269,0.0007098605,0.001641753,0.001259048,0.0009679542,0.001203458],"category_scores_gemma":[0.01481372,0.0002590387,0.0005839691,0.002170315,0.000482015,0.002462215,0.001228126,0.001430676,0.0009297871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006789943,"about_ca_system_score_gemma":0.0009185051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002697461,"about_ca_topic_score_gemma":0.004354744,"domain_scores_codex":[0.9978014,0.0004170725,0.0002332714,0.000700354,0.0006588902,0.0001890454],"domain_scores_gemma":[0.981169,0.009175386,0.002600494,0.002175383,0.004448128,0.0004315511],"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":[0.0005955386,0.0003201735,0.1359384,0.0003333117,0.00008396322,0.0002939852,0.001230695,0.007202826,0.01980274,0.001765664,0.007945007,0.8244877],"study_design_scores_gemma":[0.00008001528,0.0005487851,0.1623039,0.0001870773,0.0001657688,0.001487194,0.001822898,0.7412757,0.06105384,0.01419681,0.01672332,0.0001548526],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7340001,0.001058846,0.2454899,0.0004854315,0.0001985377,0.0004202518,0.002125129,0.01101717,0.005204685],"genre_scores_gemma":[0.8709643,0.0001922892,0.1204724,0.00009477454,0.00007179128,0.0001603656,0.005000554,0.0002988494,0.00274462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007089269,"threshold_uncertainty_score":0.01135069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189253959451926,"score_gpt":0.2734799224548978,"score_spread":0.2615873828603786,"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."}}