{"id":"W2101121486","doi":"10.1109/compsac.2011.69","title":"Reasoning about Global Clones: Scalable Semantic Clone Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Computer science; Semantic reasoner; clone (Java method); Context (archaeology); Scalability; Semantic Web; SPARQL; Data mining; Software engineering; Information retrieval; World Wide Web; Database; Artificial intelligence; RDF","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":[0.002893659,0.001196128,0.001433807,0.004118919,0.001142817,0.0025248,0.002375954,0.001811451,0.001263188],"category_scores_gemma":[0.01407958,0.0005787478,0.001718289,0.003107624,0.001107284,0.005021412,0.002901182,0.001361502,0.0005112285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074882,"about_ca_system_score_gemma":0.001937717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006322196,"about_ca_topic_score_gemma":0.00676586,"domain_scores_codex":[0.9963426,0.0005436924,0.0002885521,0.0008811129,0.001725868,0.000218166],"domain_scores_gemma":[0.9898201,0.00507282,0.001108003,0.002191002,0.00157343,0.0002346252],"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":[0.0004314765,0.0003034922,0.02706902,0.000449781,0.0002721784,0.0008285458,0.001348212,0.07768764,0.02836172,0.01482107,0.008434063,0.8399929],"study_design_scores_gemma":[0.00008365574,0.0001075841,0.003323596,0.00004211786,0.0001638664,0.0006129103,0.0004849426,0.9195247,0.0342126,0.03509806,0.006284445,0.00006145382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06367117,0.0003047256,0.9191452,0.0003456611,0.00003064578,0.0001643025,0.0004668041,0.01487069,0.001000811],"genre_scores_gemma":[0.3265302,0.0001798248,0.6691797,0.000182832,0.00002983893,0.0001298324,0.001740755,0.0006700784,0.001356917],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006322196,"threshold_uncertainty_score":0.01530331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003191092728848,"score_gpt":0.2492118094169868,"score_spread":0.2291798984896984,"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."}}