{"id":"W2093518292","doi":"10.1016/j.neurobiolaging.2012.11.002","title":"Age dependence of hemodynamic response characteristics in human functional magnetic resonance imaging","year":2012,"lang":"en","type":"article","venue":"Neurobiology of Aging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Functional magnetic resonance imaging; Haemodynamic response; Hypercapnia; Hemodynamics; Blood-oxygen-level dependent; Magnetic resonance imaging; Cerebral blood flow; Psychology; Premovement neuronal activity; Brain mapping; Neuroscience; Functional imaging; Brain activity and meditation; Cardiology; Audiology; Internal medicine; Medicine; Electroencephalography; Heart rate; Blood pressure; Respiratory system","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":[],"consensus_categories":[],"category_scores_codex":[0.0002090792,0.00007583344,0.0002002699,0.0001129515,0.00002778932,8.649063e-7,0.00006229019,0.00003767081,0.00002688833],"category_scores_gemma":[0.00004787858,0.00007593815,0.00003263744,0.0001162095,0.0001609096,0.00004396345,0.00004434311,0.0001597071,0.000001446345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002357634,"about_ca_system_score_gemma":0.00001941162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009985088,"about_ca_topic_score_gemma":0.000001656324,"domain_scores_codex":[0.9993159,0.00004735138,0.000269531,0.0001381527,0.00004984884,0.0001791949],"domain_scores_gemma":[0.9995161,0.000106677,0.0001104872,0.000197644,0.00003647043,0.00003261024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001300054,0.0001002952,0.267744,0.00002354493,6.691313e-7,0.000007861379,0.00006937399,0.000005203983,0.7280241,0.0008257149,0.00002170355,0.00304752],"study_design_scores_gemma":[0.0002931968,0.00008570431,0.973636,0.00008136945,0.00001020636,0.00006226589,0.00001984117,0.0001057105,0.02492709,0.0001871812,0.0005347427,0.00005671735],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960661,0.0003806133,0.002872875,0.0002590533,0.00004121978,0.0001452863,0.000009564568,0.0000291314,0.0001961436],"genre_scores_gemma":[0.9949838,0.00003320507,0.004629565,0.0001608409,0.00002983284,0.00001504858,0.00001716428,0.00001070694,0.0001198219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.705892,"threshold_uncertainty_score":0.3096668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739811147365576,"score_gpt":0.2981446007005807,"score_spread":0.2807464892269249,"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."}}