{"id":"W4310191118","doi":"10.3389/fnana.2022.960475","title":"Histology-informed automatic parcellation of white matter tracts in the rat spinal cord","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroanatomy","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Polytechnique Montréal","funders":"Canadian Institutes of Health Research; Craig H. Neilsen Foundation; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Polytechnique Montréal; Réseau en Bio-Imagerie du Quebec","keywords":"White matter; Axon; Spinal cord; Morphometrics; Neuroscience; Anatomy; Segmentation; Biology; Myelin; Brain atlas; Corticospinal tract; Cluster analysis; Artificial intelligence; Diffusion MRI; Central nervous system; Pattern recognition (psychology); Computer science; Magnetic resonance imaging; Medicine; Zoology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001466885,0.00009219014,0.0002071511,0.0002555364,0.00008494805,0.000003451004,0.0001898235,0.00002284617,0.00007712175],"category_scores_gemma":[0.00002889333,0.00007938044,0.0000462235,0.0004693627,0.00007626924,0.00005566047,0.00006458132,0.000367403,0.000002124606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001051394,"about_ca_system_score_gemma":0.00005267886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007350474,"about_ca_topic_score_gemma":0.000001871536,"domain_scores_codex":[0.9990609,0.00008010741,0.0003166081,0.0001792539,0.000196889,0.000166184],"domain_scores_gemma":[0.9994692,0.00003722738,0.0001318365,0.0003225561,0.00001461753,0.0000245786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004721451,0.0003661466,0.8558242,0.000175601,0.000007711415,0.000169765,0.0008750303,0.0001751066,0.0004250303,0.0005213711,0.1251743,0.01581367],"study_design_scores_gemma":[0.001755002,0.0007176062,0.8245226,0.00006231917,0.000050477,0.0003879288,0.0008507495,0.01468996,0.000296032,0.004177398,0.1522731,0.0002168078],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833854,0.0001639388,0.003163193,0.007773649,0.0002624083,0.001029941,0.000005560171,0.00006934781,0.004146568],"genre_scores_gemma":[0.9882933,0.0000203361,0.009082063,0.00216451,0.00001017292,0.0001872657,0.00001472825,0.00001437884,0.0002132818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03130153,"threshold_uncertainty_score":0.323704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0336369586982366,"score_gpt":0.3281442183020419,"score_spread":0.2945072596038053,"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."}}