{"id":"W1984610995","doi":"10.1109/csmr-wcre.2014.6747198","title":"Analysis and clustering of model clones: An automotive industrial experience","year":2014,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"clone (Java method); Automotive industry; Cluster analysis; Computer science; Similarity (geometry); Cluster (spacecraft); Data mining; Artificial intelligence; Engineering; Programming language","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":[0.0003201883,0.00007447554,0.0001581836,0.0002500013,0.00003413761,0.000065612,0.0004895534,0.00005049397,0.000006657588],"category_scores_gemma":[0.0003730824,0.00006667543,0.00003170571,0.0007393198,0.00004689145,0.0003971739,0.0002975839,0.00008532126,0.0000010619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001539803,"about_ca_system_score_gemma":0.00002429165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001292092,"about_ca_topic_score_gemma":0.00001975032,"domain_scores_codex":[0.9991211,0.00003922789,0.0001397715,0.000281509,0.0002473913,0.0001709876],"domain_scores_gemma":[0.9991084,0.0002345255,0.00002921336,0.0004423589,0.00007936251,0.0001061726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001725223,0.0001146123,0.2192758,0.00002681157,0.0001940201,0.000004064153,0.01493718,0.665914,0.002461245,0.006452452,0.00004061437,0.09056201],"study_design_scores_gemma":[0.0001370206,0.00005519701,0.01420214,0.000003442323,0.000007260522,9.610739e-7,0.00004027651,0.9833569,0.002035815,0.00008050574,0.000005094239,0.00007536427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4111553,0.000002509466,0.5886203,0.00001933974,0.00002614259,0.00002897194,3.053053e-7,0.00008323949,0.00006393537],"genre_scores_gemma":[0.9236403,0.000001152015,0.07625882,0.00001768067,0.00001930038,0.000005940097,4.358587e-7,0.000003762251,0.00005258751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.512485,"threshold_uncertainty_score":0.2718945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04999636713523831,"score_gpt":0.3028479037451183,"score_spread":0.25285153660988,"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."}}