{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001915081,0.00142847,0.001059641,0.00902984,0.0007621374,0.001989633,0.002202487,0.001426388,0.004785916],"category_scores_gemma":[0.01510977,0.0008110992,0.001018034,0.004971636,0.0006614286,0.003238508,0.002410434,0.001256348,0.002577473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053366,"about_ca_system_score_gemma":0.001552256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005400058,"about_ca_topic_score_gemma":0.008677204,"domain_scores_codex":[0.9981059,0.0001544387,0.0002360396,0.0005241368,0.000866272,0.0001131868],"domain_scores_gemma":[0.9905059,0.003968589,0.001499939,0.001919181,0.001779689,0.0003267389],"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":[0.0008857146,0.0002680749,0.05279237,0.0009540782,0.0003279645,0.0006948106,0.001708944,0.0105859,0.03187557,0.006121087,0.07413517,0.8196504],"study_design_scores_gemma":[0.0002907251,0.0004262454,0.0413755,0.0002727549,0.0002586601,0.001713616,0.0007234219,0.6580319,0.1403454,0.02129447,0.1349426,0.0003248343],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04820361,0.0008011204,0.566224,0.0003160007,0.0001006928,0.0003839196,0.01467854,0.3669049,0.002387064],"genre_scores_gemma":[0.1809925,0.0003220604,0.7738051,0.0003375403,0.00008706033,0.0006474567,0.02517808,0.013913,0.004717098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00902984,"threshold_uncertainty_score":0.01601052,"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."}}