{"id":"W4413183765","doi":"10.1101/2025.08.06.668521","title":"A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of British Columbia","funders":"National Institute on Aging; National Institutes of Health; National Health and Medical Research Council; Norges Forskningsråd; Medical University of South Carolina; National Imaging Facility; Bundesministerium für Bildung und Forschung; Australian Rotary Health; Swinburne University of Technology; University of New South Wales; Deutsche Forschungsgemeinschaft; John S. Dunn Foundation; European Commission; National Institute of Mental Health; Ministerio de Ciencia, Tecnología e Innovación; University of Texas Health Science Center at Houston","keywords":"Geometry; Surface (topology); Scalability; Computer science; Computer graphics (images); Mathematics; Database","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":[0.0009646051,0.001261354,0.001058593,0.001274612,0.0006350765,0.002710688,0.003466901,0.001072282,0.01377857],"category_scores_gemma":[0.003516847,0.001070466,0.002041338,0.001279991,0.0007112612,0.001278069,0.003593454,0.002119335,0.006383047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000620059,"about_ca_system_score_gemma":0.001959886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00786925,"about_ca_topic_score_gemma":0.01524207,"domain_scores_codex":[0.9994414,0.00008761788,0.0000497365,0.00008206484,0.000297617,0.00004156171],"domain_scores_gemma":[0.9991056,0.0003660944,0.00006482389,0.000197525,0.0001932718,0.00007258097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003257247,0.0001681944,0.004068796,0.001336375,0.0007549333,0.0009778576,0.001004485,0.4338409,0.03296673,0.1001573,0.1662523,0.2581465],"study_design_scores_gemma":[0.00005717692,0.00001918309,0.0007198635,0.00005315521,0.00002825613,0.00022063,0.00006353303,0.9122463,0.004242893,0.04064343,0.04165571,0.00004983275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002081396,0.000114877,0.9688571,0.0001601978,0.00004656871,0.00009110991,0.002285991,0.0250703,0.001292529],"genre_scores_gemma":[0.07531743,0.0005391774,0.8978363,0.0002322183,0.00005626622,0.0009943349,0.008260477,0.01205402,0.004709745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01377857,"threshold_uncertainty_score":0.04609388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04844747689932018,"score_gpt":0.305939608424462,"score_spread":0.2574921315251418,"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."}}