{"id":"W4408293277","doi":"10.52294/001c.130075","title":"Understanding variability in brain MRI templates: Optimal sample sizes for representative population averages","year":2025,"lang":"en","type":"article","venue":"Aperture Neuro","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Canada First Research Excellence Fund; McGill University","keywords":"Template; Sample (material); Sample size determination; Population; Computer science; Statistics; Artificial intelligence; Mathematics; Medicine; Chromatography; Chemistry","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03282506,0.0004987952,0.0009599728,0.001039164,0.0005037003,0.001158919,0.001231923,0.001547652,0.001208294],"category_scores_gemma":[0.1592968,0.0004349818,0.0007555989,0.0008137872,0.001136779,0.001911289,0.001266814,0.001145597,0.000338171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004066035,"about_ca_system_score_gemma":0.0008053109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000728411,"about_ca_topic_score_gemma":0.0008826593,"domain_scores_codex":[0.9915115,0.005518829,0.0004928034,0.001481876,0.0008496791,0.0001452356],"domain_scores_gemma":[0.9077373,0.07908829,0.002388272,0.006712046,0.003602124,0.0004720004],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002944583,0.0005497859,0.05918938,0.001041517,0.001019746,0.0006434554,0.004626719,0.1644331,0.04622698,0.04036399,0.007780937,0.6711799],"study_design_scores_gemma":[0.000551849,0.001720747,0.05706415,0.0003309277,0.0004994796,0.00118409,0.0008333895,0.7687234,0.03637965,0.1232077,0.009336528,0.0001681929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0843356,0.0003515862,0.9132704,0.000243054,0.0000533092,0.0003737124,0.0002002151,0.000568938,0.0006032081],"genre_scores_gemma":[0.4651357,0.000202333,0.531774,0.0001668696,0.00007022334,0.001413258,0.0007146214,0.0002539612,0.0002690449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9671749,"threshold_uncertainty_score":0.1735976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1310970564080618,"score_gpt":0.4027128570905373,"score_spread":0.2716158006824755,"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."}}