{"id":"W2564109322","doi":"10.1101/097287","title":"Spatial specificity of the functional MRI blood oxygenation response relative to neuronal activity","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ocular dominance column; Voxel; Pattern recognition (psychology); Artificial intelligence; Visual cortex; Blood-oxygen-level dependent; Functional magnetic resonance imaging; Receptive field; Image resolution; Resting state fMRI; Computer science; Nuclear magnetic resonance; Neuroscience; Physics; Biological system; Psychology; Ocular dominance; Biology","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":[0.001092198,0.000207469,0.0001944774,0.0004657742,0.0001200061,0.0005036435,0.0002381758,0.0003872103,0.0007646519],"category_scores_gemma":[0.003448029,0.0001870429,0.0001221758,0.0001630879,0.0007401137,0.0004133461,0.0003319428,0.00036007,0.0001737944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002767309,"about_ca_system_score_gemma":0.0002431547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009773737,"about_ca_topic_score_gemma":0.0009605427,"domain_scores_codex":[0.9996949,0.00008359583,0.00001452642,0.000107697,0.00006328309,0.00003595645],"domain_scores_gemma":[0.9988997,0.0006871818,0.0001480473,0.00009896525,0.000113306,0.00005274806],"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":[0.0002579926,0.000032985,0.02051133,0.00008427883,0.0000526864,0.0001298321,0.0001203149,0.01545139,0.9450817,0.003228257,0.0001679696,0.01488129],"study_design_scores_gemma":[0.00001849171,0.0002154118,0.2099968,0.00002913641,0.00006011099,0.001137519,0.000117604,0.1825058,0.5939364,0.0110968,0.0008387166,0.00004724478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8177003,0.000342983,0.179889,0.0001437271,0.00000950497,0.00001871253,0.0001510198,0.0002387166,0.001505895],"genre_scores_gemma":[0.9942513,0.0000613199,0.00537206,0.00002545313,0.000004307259,0.000008440526,0.00005988355,0.00001939022,0.0001979659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001092198,"threshold_uncertainty_score":0.005776167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017670464366857,"score_gpt":0.253468031082156,"score_spread":0.2332913264384874,"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."}}