{"id":"W2038172274","doi":"10.1109/icsm.2013.79","title":"gCad: A Near-Miss Clone Genealogy Extractor to Support Clone Evolution Analysis","year":2013,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"clone (Java method); Extractor; Computer science; Programming language; Biology; Genetics; Engineering; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004658189,0.0001636755,0.0002669872,0.0004689341,0.00009223865,0.000314663,0.001025243,0.0001016005,0.00108978],"category_scores_gemma":[0.0003538581,0.0001518284,0.0001278207,0.002489065,0.00003263295,0.0004937024,0.0003886473,0.0001819112,0.002960292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00018227,"about_ca_system_score_gemma":0.0001414247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001629098,"about_ca_topic_score_gemma":0.00004209488,"domain_scores_codex":[0.9979168,0.0000663744,0.0002760017,0.0005680723,0.0005507034,0.0006221067],"domain_scores_gemma":[0.9979113,0.0003693709,0.0000335959,0.001007341,0.0002453241,0.0004330708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005128857,0.0009928022,0.5216947,0.000182184,0.002154282,0.0002759972,0.002396297,0.04935449,0.1274055,0.02974971,0.1576816,0.1080611],"study_design_scores_gemma":[0.0002193223,0.0002130016,0.875878,0.000003738744,0.00003026728,0.00001818494,0.000008555554,0.1189726,0.001508896,0.0003361684,0.002495033,0.0003162037],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3271604,0.0000312652,0.6710448,0.0007660678,0.0001490825,0.0002278232,0.000001547736,0.0003849337,0.0002341362],"genre_scores_gemma":[0.8721269,0.000002744544,0.1260174,0.0001633651,0.00006326631,0.00008248812,0.000006808174,0.00001368308,0.001523304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5450274,"threshold_uncertainty_score":0.9998233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545080715003185,"score_gpt":0.2707704179751598,"score_spread":0.2553196108251279,"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."}}