{"id":"W874041938","doi":"10.1016/j.bandl.2015.06.004","title":"Structural correlates of spoken language abilities: A surface-based region-of interest morphometry study","year":2015,"lang":"en","type":"article","venue":"Brain and Language","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Institut Universitaire en Santé Mentale de Québec","funders":"National Institute on Deafness and Other Communication Disorders; University of Chicago","keywords":"Psychology; Fluency; Insula; Verbal fluency test; Sentence; Spoken language; Frontal lobe; Brain morphometry; Functional magnetic resonance imaging; Neuroimaging; Parietal lobe; Cognitive psychology; Audiology; Magnetic resonance imaging; Neuroscience; Linguistics; Neuropsychology; Cognition; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003267302,0.0001788257,0.0003451474,0.0001328664,0.00003816087,0.00001708528,0.0002652939,0.00008241451,0.00007047735],"category_scores_gemma":[0.001552892,0.0001369771,0.0000699439,0.0002574645,0.0003731672,0.00007725638,0.000104925,0.0001721597,0.000003929234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001267891,"about_ca_system_score_gemma":0.00005388638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004720604,"about_ca_topic_score_gemma":0.0001068943,"domain_scores_codex":[0.9986464,0.0003004568,0.0003269001,0.0003593749,0.000149877,0.0002169555],"domain_scores_gemma":[0.9984771,0.0008147469,0.0001786649,0.0003814113,0.00004117159,0.0001069131],"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":[0.0006828455,0.0006220346,0.08557869,0.000286952,0.00004509016,0.004713788,0.1656783,0.00006948168,0.7377245,0.0004118263,0.0009780969,0.003208394],"study_design_scores_gemma":[0.009659197,0.00421922,0.01515103,0.0002637256,0.0001139843,0.001392162,0.2850295,0.001227299,0.6812571,0.0003851703,0.0002082355,0.00109335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982917,0.0006214435,0.000005676672,0.0001347796,0.0001776263,0.000336738,0.00005177515,0.00005925224,0.0003210586],"genre_scores_gemma":[0.9988907,0.000002216962,0.00007501906,0.0004412738,0.00004211067,0.000002774067,0.000007973662,0.00001798757,0.0005199593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1193512,"threshold_uncertainty_score":0.5585765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07522318785522966,"score_gpt":0.3097941976776037,"score_spread":0.234571009822374,"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."}}