{"id":"W577586447","doi":"10.1016/j.dib.2015.05.019","title":"Quantitative analysis of the myelin g -ratio from electron microscopy images of the macaque corpus callosum","year":2015,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Université Laval; Montreal Neurological Institute and Hospital; McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Myelin; Corpus callosum; Axon; Macaque; Magnetic resonance imaging; Electron microscope; Stereology; Anatomy; Myelin sheath; Aspect ratio (aeronautics); Biology; Pathology; Chemistry; Nuclear magnetic resonance; Materials science; Medicine; Physics; Neuroscience; Central nervous system; Optics; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004581912,0.0003919041,0.0002170207,0.003205898,0.0003540352,0.0005253452,0.0002152086,0.0003454249,0.001486265],"category_scores_gemma":[0.0007356412,0.0001558084,0.0001714265,0.0008651291,0.0002869939,0.0003945128,0.0003039223,0.0002370763,0.0002162424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003048286,"about_ca_system_score_gemma":0.000180932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001954528,"about_ca_topic_score_gemma":0.002568338,"domain_scores_codex":[0.9998584,0.00001707518,0.00001403171,0.00003243712,0.00005626166,0.00002174766],"domain_scores_gemma":[0.9995554,0.0000939883,0.0001063611,0.00004190277,0.0001686084,0.0000336679],"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":[0.0001371233,0.00001810876,0.002977965,0.0002486157,0.00005845524,0.000185014,0.0002326206,0.0007500521,0.9746208,0.0004042787,0.0001660443,0.02020096],"study_design_scores_gemma":[0.00001918319,0.000436915,0.299171,0.000132096,0.0002651422,0.004672645,0.0006332578,0.02378781,0.6626011,0.001811807,0.0063775,0.00009159235],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602956,0.001891003,0.0342728,0.00007319839,0.00001584893,0.00006074276,0.0008998036,0.0004705138,0.002020563],"genre_scores_gemma":[0.9416523,0.001182495,0.05460027,0.00003599386,0.00002400536,0.00006250446,0.0007751942,0.0001754767,0.001491703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003205898,"threshold_uncertainty_score":0.0049721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1172412816665624,"score_gpt":0.419422884857553,"score_spread":0.3021816031909906,"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."}}