{"id":"W4400689326","doi":"10.1007/s00521-024-10097-2","title":"Retraction Note: A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":true,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Ensemble learning; Machine learning","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":"high","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":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005008128,0.00170261,0.001730187,0.001554237,0.001376771,0.001788945,0.004161882,0.008379205,0.012599],"category_scores_gemma":[0.07966644,0.0005755561,0.001875064,0.001285485,0.001962418,0.001821649,0.001456927,0.0193811,0.01029604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002515913,"about_ca_system_score_gemma":0.003950085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01030509,"about_ca_topic_score_gemma":0.01030483,"domain_scores_codex":[0.9973124,0.000487099,0.0005681416,0.0004312469,0.0009803154,0.0002207851],"domain_scores_gemma":[0.9706692,0.01164716,0.001110626,0.00116965,0.01397813,0.001425289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004070958,0.000005115867,0.00007061415,0.00008210132,0.0000200691,0.0001809152,0.00002604377,0.00007742559,0.0001328322,0.0005272884,0.9910349,0.00780196],"study_design_scores_gemma":[0.000125925,0.00009151764,0.002368751,0.0004745116,0.0001632453,0.001420561,0.0001320282,0.004008136,0.001591523,0.004238468,0.9852781,0.0001073199],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0003682672,0.002090184,0.002242424,0.1476214,0.8461168,0.00002830667,0.0006367581,0.0002027415,0.0006930703],"genre_scores_gemma":[0.0139022,0.006186197,0.007169335,0.1179332,0.7856266,0.0002479721,0.001084601,0.0005409856,0.06730897],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.9916208,"threshold_uncertainty_score":0.04214782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02444328655197795,"score_gpt":0.3320954582427867,"score_spread":0.3076521716908088,"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."}}