{"id":"W3212993940","doi":"10.1002/hbm.25675","title":"A sub+cortical fMRI‐based surface parcellation","year":2021,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Neuroimaging; Psychology; Neuroscience; Artificial intelligence; White matter; Pattern recognition (psychology); Cartography; Computer science; Geography; Magnetic resonance imaging","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003831509,0.0001565838,0.0001842889,0.00006564434,0.0007837771,0.00009648052,0.0001177172,0.00005802808,0.000263105],"category_scores_gemma":[0.009635657,0.0001755663,0.00009562918,0.0004203001,0.0001383008,0.0001267777,0.0001143247,0.0002444691,0.000166825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008496459,"about_ca_system_score_gemma":0.00007683112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005692846,"about_ca_topic_score_gemma":0.00002371712,"domain_scores_codex":[0.9980472,0.0003948399,0.0002292082,0.0006233786,0.0003750328,0.0003303515],"domain_scores_gemma":[0.9932945,0.006168691,0.00006928353,0.0003140018,0.00008024101,0.00007322506],"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":[0.000006809847,0.00005833095,0.002238265,0.00002836705,0.00000596537,0.00006471475,0.0001782552,0.000772281,0.9817556,0.007980511,0.006831205,0.00007972251],"study_design_scores_gemma":[0.001715777,0.0001273152,0.2598607,0.0001663981,0.00002220435,0.00004847801,0.0004954861,0.01815161,0.6455818,0.01030348,0.06259776,0.0009289825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649469,0.00003935549,0.006998991,0.01984566,0.0002536119,0.0001620251,0.000005291946,0.0002334365,0.007514675],"genre_scores_gemma":[0.9883521,0.000001687468,0.0003686217,0.01002027,0.0001440899,0.000009999319,0.000005780458,0.00002252391,0.001074893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3361737,"threshold_uncertainty_score":0.9987066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08209998146911505,"score_gpt":0.2891341646786639,"score_spread":0.2070341832095489,"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."}}