{"id":"W4407126910","doi":"10.1007/s13246-024-01501-1","title":"Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images","year":2025,"lang":"en","type":"erratum","venue":"Physical and Engineering Sciences in Medicine","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Electroencephalography; Computer science; Schizophrenia (object-oriented programming); Artificial intelligence; Transfer of learning; Channel (broadcasting); Distillation; Transfer (computing); Pattern recognition (psychology); Machine learning; Psychology; Chemistry; Neuroscience; Chromatography; Telecommunications","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":[0.0002203643,0.0001835805,0.0003403846,0.0003369504,0.0001308928,0.00004714027,0.00007743782,0.00007404342,2.410854e-7],"category_scores_gemma":[0.0005379228,0.0001476925,0.00002229618,0.0005323444,0.0002257867,0.0001192581,0.00004130695,0.0002757195,3.963131e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002341552,"about_ca_system_score_gemma":0.00001777206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000584586,"about_ca_topic_score_gemma":0.000007968283,"domain_scores_codex":[0.9989578,0.00003254246,0.0002033064,0.0004320464,0.0001809552,0.0001933402],"domain_scores_gemma":[0.9993978,0.0004315734,0.00004354998,0.0000440988,0.00003077815,0.00005222303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005182246,0.00007317813,0.00003133981,0.0009925045,0.00001157897,0.000001608662,0.001862531,0.03650947,0.9380264,0.00006046379,0.0008466268,0.02153253],"study_design_scores_gemma":[0.0002823872,0.0006241286,0.0008395488,0.001263611,0.00003014283,0.00001148215,0.00005364624,0.9813636,0.01481267,0.00007518936,0.0004947392,0.0001488803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453087,0.0005699862,0.04420124,0.0001895176,0.008733998,0.0004365834,0.000008647285,0.0003363635,0.0002149834],"genre_scores_gemma":[0.9987605,0.00009154085,0.0006365029,0.0000198399,0.0002921619,0.00000882228,0.000002333391,0.000009516828,0.0001787483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9448541,"threshold_uncertainty_score":0.6022727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01662350297725248,"score_gpt":0.279813587175864,"score_spread":0.2631900841986115,"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."}}