{"id":"W2001467170","doi":"10.1016/j.neuroimage.2010.08.070","title":"Improved fMRI calibration: Precisely controlled hyperoxic versus hypercapnic stimuli","year":2010,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network; McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Hypercapnia; Cerebral blood flow; Arterial spin labeling; Hyperoxia; Functional magnetic resonance imaging; Chemistry; Oxygen; Carbon dioxide; Default mode network; Calibration; Magnetic resonance imaging; Anesthesia; Venous blood; Nuclear magnetic resonance; Biomedical engineering; Cardiology; Internal medicine; Medicine; Neuroscience; Physics; Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008571269,0.000689333,0.0003719298,0.0002705542,0.0002734892,0.0009723743,0.0007441534,0.001118153,0.00307795],"category_scores_gemma":[0.004550117,0.0004232998,0.0001550617,0.0002842839,0.0005652517,0.001159507,0.0008661324,0.0008111058,0.0003822057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002649869,"about_ca_system_score_gemma":0.0004273629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003707425,"about_ca_topic_score_gemma":0.000534242,"domain_scores_codex":[0.9994975,0.0001390578,0.00003262919,0.0001472382,0.0001248328,0.00005868977],"domain_scores_gemma":[0.9992025,0.000482752,0.00009508694,0.00009170158,0.00007980738,0.00004810851],"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":[0.002076956,0.0001932147,0.0005301359,0.0002396825,0.0000258057,0.00005400274,0.0001014152,0.002578998,0.9498563,0.0009122942,0.0005093451,0.04292172],"study_design_scores_gemma":[0.0002290519,0.0007902673,0.009141777,0.0000850539,0.00009322415,0.0006168989,0.00006123014,0.03231335,0.9500366,0.001737015,0.004789685,0.0001059503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.515862,0.003101621,0.4695767,0.00118182,0.0006539513,0.000466781,0.0003959625,0.001741279,0.007019845],"genre_scores_gemma":[0.9111603,0.001018397,0.08401646,0.0009793737,0.0001855161,0.0002239263,0.0001278756,0.0006204444,0.001667767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00307795,"threshold_uncertainty_score":0.01029676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03218443372392886,"score_gpt":0.3263207072565878,"score_spread":0.2941362735326589,"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."}}