{"id":"W4396228166","doi":"10.3389/fnhum.2024.1417744","title":"Corrigendum: Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization","year":2024,"lang":"en","type":"erratum","venue":"Frontiers in Human Neuroscience","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; Université de Montréal; SickKids Foundation; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Spelling; Computer science; Classifier (UML); Visualization; Electroencephalography; Natural language processing; Artificial intelligence; Feature (linguistics); Speech recognition; Psychology; Linguistics; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002832105,0.001927467,0.00107307,0.002568581,0.001718913,0.00345738,0.002031669,0.002859299,0.1013117],"category_scores_gemma":[0.06533915,0.0006942677,0.001068228,0.001759821,0.001269846,0.001836515,0.001797194,0.00352172,0.08668368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001826701,"about_ca_system_score_gemma":0.00280872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01346853,"about_ca_topic_score_gemma":0.01888897,"domain_scores_codex":[0.996614,0.000486136,0.0004902498,0.0005974131,0.001662002,0.0001502953],"domain_scores_gemma":[0.9641739,0.007265365,0.0009219274,0.003093227,0.02384257,0.0007030241],"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":[0.00002644708,0.00000871596,0.000180448,0.00007661539,0.000009527971,0.0002538147,0.00002872937,0.0002040697,0.0002963497,0.000452474,0.9702946,0.02816826],"study_design_scores_gemma":[0.00003451231,0.00005036322,0.001561359,0.0003262393,0.00005694472,0.001944631,0.0001383876,0.005434548,0.004381922,0.003184156,0.9827933,0.00009360633],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.002911082,0.00158273,0.1128725,0.0643611,0.7546501,0.000245191,0.01496633,0.02896006,0.01945086],"genre_scores_gemma":[0.04477795,0.004868171,0.2073511,0.04674231,0.09379653,0.0005959251,0.03814876,0.03786843,0.5258508],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1013117,"threshold_uncertainty_score":0.3389212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1169534508750331,"score_gpt":0.3918434316436969,"score_spread":0.2748899807686639,"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."}}