{"id":"W1512857087","doi":"10.1109/iwpse.2004.3","title":"Aiding comprehension of cloning through categorization","year":2004,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"clone (Java method); Program comprehension; Computer science; False positive paradox; Software maintenance; Cloning (programming); Source code; Function (biology); Software; Programming language; Categorization; Filter (signal processing); Software system; Artificial intelligence; Biology; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009688607,0.00004793159,0.00006790473,0.00005453464,0.00004123366,0.0000279128,0.0002938493,0.00002566167,0.000004936771],"category_scores_gemma":[0.0001122323,0.0000438082,0.00001891331,0.0003839821,0.00001428113,0.0003496054,0.0001358293,0.0000597106,0.00001713306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004036853,"about_ca_system_score_gemma":0.00003660616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169747,"about_ca_topic_score_gemma":0.000001189117,"domain_scores_codex":[0.9993997,0.000009549352,0.0001055698,0.0001386844,0.000215105,0.0001314032],"domain_scores_gemma":[0.9995189,0.0001290315,0.00002141678,0.0002365241,0.00007017068,0.00002400275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002218717,0.00005425406,0.005443527,0.00005941397,0.00001168302,0.000009992023,0.001918878,0.1330809,0.06108961,0.7937059,0.0001046146,0.004519015],"study_design_scores_gemma":[0.001215987,0.0002327143,0.03313394,0.0001663946,0.000003943683,0.0000353208,0.0000761252,0.07491925,0.8472881,0.04181205,0.0007215621,0.000394633],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123417,0.00003762597,0.8756312,0.0001495996,0.0001282965,0.00004577663,6.438684e-8,0.0001988351,0.0003915444],"genre_scores_gemma":[0.8219745,0.000004844866,0.1779561,0.00002163012,0.0000135823,0.000001405458,6.841361e-7,0.000003981724,0.00002335683],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7861985,"threshold_uncertainty_score":0.1786447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673080414723087,"score_gpt":0.2748554369435063,"score_spread":0.2481246327962754,"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."}}