{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001121412,0.0002846625,0.0002939763,0.0002306327,0.0003774572,0.001098651,0.00039913,0.0004110959,0.0001474903],"category_scores_gemma":[0.0009403998,0.0003196521,0.00006256717,0.001545561,0.0000842118,0.001962421,0.0004276367,0.0009949632,0.00006886821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001757397,"about_ca_system_score_gemma":0.00031314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002398663,"about_ca_topic_score_gemma":0.0001008325,"domain_scores_codex":[0.9965826,0.0002313118,0.000354842,0.001302213,0.000811705,0.0007173563],"domain_scores_gemma":[0.9971883,0.000465853,0.0000708767,0.001201259,0.0006461891,0.0004275644],"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":[0.0001256668,0.002066938,0.006316133,0.001090925,0.0002398432,0.0004636919,0.00298608,0.05040682,0.1786379,0.00650817,0.00122073,0.7499371],"study_design_scores_gemma":[0.0003977341,0.0002570154,0.03625548,0.00002983035,0.00002251523,0.0004623024,0.0002771165,0.9033571,0.05631628,0.0001505851,0.002092485,0.0003815673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1077476,0.00115002,0.8866392,0.0001514687,0.001728468,0.0002349389,0.000002534888,0.0003808516,0.001964844],"genre_scores_gemma":[0.8887402,0.0002628579,0.1051774,0.00006624748,0.0003014902,0.00001466171,0.000004969185,0.00003640043,0.005395838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8529503,"threshold_uncertainty_score":0.9999383,"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."}}