{"id":"W7128625888","doi":"10.1109/icces51350.2021.11391692","title":"Retraction Notice: An Adaptable and Extensible Code Smell Detection Approach","year":2021,"lang":"","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Code smell; Code refactoring; Mistake; Code (set theory); Product (mathematics); Source code","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008276798,0.001457629,0.0007615939,0.002362451,0.003466852,0.005158221,0.003344375,0.009818153,0.03624978],"category_scores_gemma":[0.1142207,0.0006200257,0.001514917,0.001307699,0.002802744,0.003833882,0.00376229,0.0155805,0.02988775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003795937,"about_ca_system_score_gemma":0.00597895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008609392,"about_ca_topic_score_gemma":0.008404668,"domain_scores_codex":[0.9901611,0.001466419,0.001232399,0.001071453,0.005487557,0.0005810717],"domain_scores_gemma":[0.9387645,0.01727593,0.002609209,0.004735116,0.03306004,0.003555278],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000166507,0.00001034935,0.000107383,0.00007722384,0.000006474073,0.000155638,0.00008886002,0.00005348527,0.0001945657,0.002848918,0.9835404,0.01290007],"study_design_scores_gemma":[0.00001201113,0.00002887127,0.000506527,0.0001574614,0.00001531106,0.0002995555,0.000102317,0.0005676934,0.0003564566,0.001891406,0.996034,0.00002847008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0008069238,0.001025707,0.008602796,0.2557513,0.7184623,0.0001499766,0.001114115,0.001657484,0.01242946],"genre_scores_gemma":[0.02547943,0.003956022,0.02825653,0.1976572,0.4177961,0.0007073347,0.002877707,0.00276604,0.3205038],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9917232,"threshold_uncertainty_score":0.1212676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0388579867604231,"score_gpt":0.2765381308201066,"score_spread":0.2376801440596835,"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."}}