{"id":"W4403706251","doi":"10.1016/j.mri.2024.110265","title":"Corrigendum to “Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices” [Magnetic Resonance Imaging 113 (2024) 110221]","year":2024,"lang":"en","type":"erratum","venue":"Magnetic Resonance Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Magnetic resonance imaging; Diffusion MRI; Diffusion-Weighted Magnetic Resonance Imaging; Nuclear magnetic resonance; White matter; Materials science; Medicine; Physics; Radiology","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":[0.002881699,0.00296797,0.002870379,0.005084748,0.004091754,0.004133008,0.004016591,0.008295508,0.1430032],"category_scores_gemma":[0.03559054,0.001467078,0.002802945,0.003094828,0.001625336,0.002558964,0.002650327,0.006973899,0.1096679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004325517,"about_ca_system_score_gemma":0.004499496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04811271,"about_ca_topic_score_gemma":0.08145376,"domain_scores_codex":[0.9961526,0.0006272346,0.0004990124,0.0006105427,0.001866166,0.0002444458],"domain_scores_gemma":[0.9816521,0.003443891,0.0005484635,0.001223786,0.0124683,0.0006634191],"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":[0.000009510462,0.000004265272,0.00001295835,0.00003635536,0.000004305178,0.00004262896,0.000004189417,0.00004504284,0.00003810707,0.0003730424,0.9965832,0.002846487],"study_design_scores_gemma":[0.00002812631,0.00002193466,0.0004936972,0.0001788269,0.00003964312,0.0001727766,0.00002606314,0.0007419891,0.0004277716,0.00394525,0.9938784,0.00004549308],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.000129081,0.002096672,0.003364305,0.03696935,0.9395062,0.00008351446,0.002291974,0.001078209,0.01448087],"genre_scores_gemma":[0.004145138,0.007315777,0.01015179,0.06482812,0.2316458,0.000337615,0.00632603,0.002911562,0.6723382],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1430032,"threshold_uncertainty_score":0.4783934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02631650777977769,"score_gpt":0.3000211244473595,"score_spread":0.2737046166675818,"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."}}