{"id":"W3098627671","doi":"10.3389/fnana.2020.599701","title":"In vivo Population Averaged Stereotaxic T2w MRI Brain Template for the Adult Yucatan Micropig","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neuroanatomy","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; Neurosurgery Research and Education Foundation; U.S. Department of Defense","keywords":"Template; Neuroimaging; Brain morphometry; Population; Computer science; Probabilistic logic; Diffusion MRI; Neuroscience; Psychology; Artificial intelligence; Magnetic resonance imaging; Medicine","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":[0.0004110254,0.0002915554,0.0002173195,0.0007798419,0.0004666178,0.0004414852,0.0005375735,0.0005292736,0.005362454],"category_scores_gemma":[0.0005487883,0.0003115218,0.0002718408,0.0004924151,0.000319647,0.0004024154,0.0004278969,0.0005418381,0.001878054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003269868,"about_ca_system_score_gemma":0.0005903634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004292239,"about_ca_topic_score_gemma":0.01794847,"domain_scores_codex":[0.9998684,0.000008294129,0.00001411829,0.00006500659,0.00003111234,0.00001316833],"domain_scores_gemma":[0.9997029,0.00002968767,0.00004357285,0.00009695643,0.0001048037,0.00002204055],"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":[0.0003799619,0.0001426349,0.01296336,0.0003140678,0.00008970194,0.001639042,0.0009004406,0.00610286,0.7779607,0.008232785,0.01384951,0.1774249],"study_design_scores_gemma":[0.00005680829,0.001053067,0.254072,0.0002623007,0.000320994,0.01561143,0.001110826,0.07506707,0.3968311,0.009066259,0.2463506,0.0001975336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.325028,0.0002921968,0.6518034,0.0002700489,0.0001496168,0.0008825558,0.008810522,0.002609606,0.01015394],"genre_scores_gemma":[0.3479836,0.0004084636,0.6233706,0.0002568256,0.0000253803,0.001905311,0.01351674,0.001264181,0.01126897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005362454,"threshold_uncertainty_score":0.01793921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105195263611685,"score_gpt":0.3151898235909438,"score_spread":0.284137870954827,"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."}}