{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001484067,0.0001117247,0.00009476033,0.00003224635,0.0008737319,0.0001359333,0.0002855364,0.00003849582,0.00007771689],"category_scores_gemma":[0.001403218,0.00005913884,0.00003243557,0.0005699634,0.0002781518,0.0001000012,0.000120036,0.0004183012,0.00004734823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007119161,"about_ca_system_score_gemma":0.00003722041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004739537,"about_ca_topic_score_gemma":0.000004063907,"domain_scores_codex":[0.9986682,0.00028328,0.0001231548,0.0003976008,0.0002437714,0.0002839967],"domain_scores_gemma":[0.9989572,0.0006613137,0.00003691173,0.0001531189,0.00004099057,0.00015044],"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":[0.00010792,0.000004074858,0.00008066669,0.000002405757,0.000001954203,0.00001193695,0.0002343142,0.0001668283,0.9775573,0.002499557,0.01721124,0.0021218],"study_design_scores_gemma":[0.000801708,0.0007075528,0.008057881,0.000003911228,0.00003426133,0.0001672067,0.0001829486,0.004369154,0.5732726,0.0008737274,0.4113287,0.0002002444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7049258,0.00005225444,0.002670572,0.2904339,0.000894081,0.0006455205,0.0001150812,0.00007499476,0.0001877548],"genre_scores_gemma":[0.8962059,0.0000293402,0.000001827148,0.1031971,0.0004213198,0.00001283196,7.390064e-7,0.00000861456,0.0001223286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4042846,"threshold_uncertainty_score":0.6720128,"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."}}