{"id":"W3094510370","doi":"10.1007/s00429-020-02153-z","title":"Unraveling the contributions to the neuromelanin-MRI contrast","year":2020,"lang":"en","type":"article","venue":"Brain Structure and Function","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"National Institute on Aging; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; University of Suwon; Universiteit Maastricht","keywords":"Neuromelanin; Locus coeruleus; Melanin; Contrast (vision); Magnetization transfer; Substantia nigra; Imaging phantom; Nuclear magnetic resonance; Neuroscience; Magnetic resonance imaging; Pathology; Chemistry; Biology; Medicine; Parkinson's disease; Nuclear medicine; Physics; Radiology; Central nervous system; Artificial intelligence; Biochemistry; Computer science; Disease","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":[0.0003170333,0.0002556001,0.0001842799,0.000221144,0.0001200987,0.0002928743,0.0001885017,0.0002794623,0.0006901543],"category_scores_gemma":[0.0006484761,0.0001374862,0.0001201924,0.0001007461,0.0003023086,0.0005399555,0.0001799127,0.0002372486,0.0001444299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001759436,"about_ca_system_score_gemma":0.0002569366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001122898,"about_ca_topic_score_gemma":0.001025128,"domain_scores_codex":[0.9999628,0.00001111338,0.000002023977,0.00001056366,0.000005836455,0.00000760932],"domain_scores_gemma":[0.999777,0.0001090035,0.00004490585,0.00001707053,0.00003163607,0.00002045535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009612646,0.00001142309,0.001383874,0.00006360221,0.000009905084,0.0001543944,0.00005407624,0.0006951205,0.9937043,0.0002634657,0.00002000121,0.003543764],"study_design_scores_gemma":[0.00001175489,0.0003690034,0.02678784,0.00003185871,0.00006068746,0.001332904,0.0001929534,0.03609453,0.9308081,0.001668299,0.002618213,0.00002390512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690322,0.003197307,0.02664814,0.0001438261,0.00001758422,0.00001493217,0.00004556056,0.00007118491,0.0008293397],"genre_scores_gemma":[0.9910508,0.0005619889,0.007911215,0.00003336008,0.00000631297,0.000006483333,0.00003099108,0.00001112891,0.0003877743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001122898,"threshold_uncertainty_score":0.002308846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099545729218779,"score_gpt":0.3074225693654737,"score_spread":0.276427112073286,"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."}}