{"id":"W2516487021","doi":"10.1098/rstb.2015.0359","title":"Validation and optimization of hypercapnic-calibrated fMRI from oxygen-sensitive two-photon microscopy","year":2016,"lang":"en","type":"article","venue":"Philosophical Transactions of the Royal Society B Biological Sciences","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Drug Abuse; National Institutes of Health; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; American Heart Association","keywords":"Computer science; Functional magnetic resonance imaging; Cerebral blood flow; Ground truth; Heuristic; Limiting; Artificial intelligence; Neuroimaging; Biological system; Neuroscience; Psychology; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002419733,0.0008786563,0.000667672,0.0004551855,0.0002934872,0.0009619875,0.00122652,0.001076723,0.0004953995],"category_scores_gemma":[0.006596646,0.0004177162,0.0004845509,0.0003188435,0.0007381253,0.0006208195,0.0007475124,0.0009424701,0.000163246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119329,"about_ca_system_score_gemma":0.001417016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006506864,"about_ca_topic_score_gemma":0.005327534,"domain_scores_codex":[0.9995421,0.0001696461,0.00002979069,0.0001109615,0.0001031672,0.0000443657],"domain_scores_gemma":[0.9981693,0.001085656,0.0001800222,0.0001897845,0.0003177658,0.00005732211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001534622,0.00007130748,0.001338179,0.000167694,0.0000543673,0.00006888321,0.00004769731,0.9654154,0.01994873,0.001137296,0.0002685299,0.01132848],"study_design_scores_gemma":[0.00002030344,0.00006390844,0.0007845339,0.00001331134,0.00001205895,0.000016243,0.000009730103,0.988624,0.009546371,0.0006670599,0.0002254654,0.00001714703],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5435413,0.000568088,0.4505617,0.0004928114,0.00005217502,0.0002038381,0.0005839624,0.001436547,0.002559531],"genre_scores_gemma":[0.921771,0.0002114525,0.07683641,0.00008650585,0.000005416276,0.0001939293,0.0004538937,0.0001027221,0.0003387188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006506864,"threshold_uncertainty_score":0.01293796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04233061703531579,"score_gpt":0.3092128380526007,"score_spread":0.2668822210172849,"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."}}