{"id":"W2946997304","doi":"10.1101/599571","title":"Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Royal College of Physicians and Surgeons of Canada","keywords":"Hippocampal formation; Hippocampus; Cluster analysis; Neuroimaging; Neuroscience; Artificial intelligence; Computer science; Laminar flow; Pattern recognition (psychology); Folding (DSP implementation); Laminar organization; Human brain; Biology; Physics","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":[0.0003486107,0.0004017693,0.0003139683,0.001679024,0.0002993083,0.0009062691,0.000424799,0.0003493607,0.000651506],"category_scores_gemma":[0.0007279796,0.0003031898,0.0004030546,0.0006949556,0.0005855595,0.0003752235,0.0006410424,0.0003970948,0.0002478685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677552,"about_ca_system_score_gemma":0.0005034716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005047418,"about_ca_topic_score_gemma":0.01078796,"domain_scores_codex":[0.9998826,0.00002017863,0.000006559311,0.00003967767,0.00002799989,0.00002296759],"domain_scores_gemma":[0.999707,0.00008101047,0.00005662426,0.00007408721,0.00005098752,0.0000302287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00050692,0.0001191368,0.035387,0.0005563452,0.0002601095,0.0007084995,0.001938172,0.1050491,0.6973367,0.006740308,0.003382875,0.1480149],"study_design_scores_gemma":[0.00002177339,0.0000932468,0.122912,0.00005973802,0.00008010345,0.000840177,0.0008830439,0.6632779,0.1949872,0.01190413,0.004831601,0.0001091065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7849279,0.0002591014,0.2112381,0.0001103423,0.00001874439,0.00005045498,0.001073654,0.001633122,0.0006886202],"genre_scores_gemma":[0.8781286,0.0001878389,0.1188523,0.00003310186,0.00001105105,0.00004724758,0.001867142,0.0003411725,0.0005315107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005047418,"threshold_uncertainty_score":0.01003605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700305336702348,"score_gpt":0.2485457776452699,"score_spread":0.2315427242782464,"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."}}