{"id":"W4293516794","doi":"10.1016/j.neuro.2022.08.010","title":"Impact of chronic exposure to legacy environmental contaminants on the corpus callosum microstructure: A diffusion MRI study of Inuit adolescents","year":2022,"lang":"en","type":"article","venue":"NeuroToxicology","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval; Université du Québec à Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"National Institute of Environmental Health Sciences; Canadian Institutes of Health Research","keywords":"Corpus callosum; Fractional anisotropy; White matter; Diffusion MRI; Methylmercury; Medicine; Physiology; Population; Magnetic resonance imaging; Internal medicine; Pathology; Environmental health; Chemistry; Environmental chemistry; Bioaccumulation; Radiology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006408157,0.0001496516,0.0002921629,0.00009466837,0.0001309904,0.000003059436,0.0002478994,0.00003416182,0.0001661632],"category_scores_gemma":[0.00002574714,0.0001087803,0.00009072103,0.0001739956,0.00009243423,0.00001758637,0.000321804,0.0003818892,0.000002438763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000238167,"about_ca_system_score_gemma":0.00006333989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003974675,"about_ca_topic_score_gemma":0.00001791999,"domain_scores_codex":[0.9988257,0.0001485405,0.0002856583,0.0003126106,0.00022772,0.0001997976],"domain_scores_gemma":[0.999116,0.00006087306,0.0001856085,0.0005572583,0.00001341492,0.00006686328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005042649,0.002686282,0.1658339,0.00001224018,0.00001630795,0.0000372773,0.0004366343,0.0001290677,0.8283718,0.0000243694,0.0001966008,0.00175124],"study_design_scores_gemma":[0.00197848,0.02547449,0.9527824,0.00001991711,0.00004086291,0.0001346763,0.0001730146,0.00006212084,0.01856693,0.00002639217,0.0006522492,0.00008845438],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960998,0.0000172682,0.00003529625,0.0003068145,0.00006954003,0.003339531,0.00009393426,0.00002808835,0.000009696216],"genre_scores_gemma":[0.9988077,0.0000111966,0.00002428993,0.0006265647,0.00002282232,0.0004238516,0.000006428797,0.00002739791,0.0000497226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8098049,"threshold_uncertainty_score":0.4435932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02814817612175857,"score_gpt":0.3256252322540136,"score_spread":0.297477056132255,"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."}}