{"id":"W2068067169","doi":"10.1016/j.neuroimage.2010.07.059","title":"Robustly measuring vascular reactivity differences with breath-hold: Normalising stimulus-evoked and resting state BOLD fMRI data","year":2010,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":142,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Medical Research Council; Medical Research Council Canada; Research Councils UK; Pfizer","keywords":"Voxel; Resting state fMRI; Cerebral blood flow; Stimulus (psychology); Psychology; Haemodynamic response; Neuroscience; Blood-oxygen-level dependent; Blood oxygenation; Analysis of variance; Functional magnetic resonance imaging; Cardiology; Blood pressure; Internal medicine; Medicine; Cognitive psychology; Artificial intelligence; Computer science; Heart rate","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007148108,0.0003397736,0.0003475135,0.0001355165,0.0007452256,0.0003441798,0.0005711659,0.00005396398,0.000009751532],"category_scores_gemma":[0.01044822,0.000286296,0.00003903026,0.0003606689,0.0004334758,0.001508613,0.000889771,0.0008252073,0.000009430281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002085392,"about_ca_system_score_gemma":0.00006621034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002765705,"about_ca_topic_score_gemma":0.0005453073,"domain_scores_codex":[0.9968572,0.0002733571,0.000232433,0.001400317,0.0007202787,0.0005163586],"domain_scores_gemma":[0.9942079,0.004160969,0.0001726377,0.001221254,0.00008671918,0.0001505255],"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":[0.0001149834,0.0001128372,0.04944455,0.00005416544,0.00001814826,0.0002313707,0.0002096022,0.0001442877,0.9454083,0.00006987212,0.0002067747,0.003985127],"study_design_scores_gemma":[0.001494262,0.0003269921,0.8723225,0.0001113807,0.00009676647,0.0004355939,0.00006164427,0.02071663,0.1013041,0.0003618979,0.001928244,0.0008400406],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951991,0.00002701916,0.000556239,0.001236913,0.000364027,0.0003050867,0.00006807688,0.0002695566,0.001973936],"genre_scores_gemma":[0.998211,0.00003730456,0.0008537789,0.0005645489,0.0001303427,0.00001233609,0.000003732501,0.0000525295,0.0001344092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8441042,"threshold_uncertainty_score":0.9999589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08186023146256252,"score_gpt":0.2655013166535772,"score_spread":0.1836410851910147,"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."}}