{"id":"W2607972699","doi":"10.1016/j.neuroimage.2017.04.011","title":"Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voxel; Decoding methods; Univariate; Multivariate statistics; Pattern recognition (psychology); Computer science; Image resolution; Artificial intelligence; Signal-to-noise ratio (imaging); Noise (video); Algorithm; Image (mathematics)","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":[0.0001380476,0.00004552719,0.0001090643,0.00003189945,0.0001582536,0.000005369297,0.00005271265,0.00002259143,0.000007511059],"category_scores_gemma":[0.000397335,0.00003813052,0.00004201845,0.00003268849,0.0001257341,0.00006321169,0.00004395493,0.00005883926,5.013785e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000120452,"about_ca_system_score_gemma":0.00002161155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000224842,"about_ca_topic_score_gemma":0.000005201416,"domain_scores_codex":[0.9995338,0.00002206549,0.0001847886,0.0001098934,0.00008759274,0.00006182647],"domain_scores_gemma":[0.9991961,0.0001154949,0.0002276706,0.0002815413,0.0001578365,0.00002140784],"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":[0.001042136,0.0001023618,0.03347341,0.00009932197,0.0000067599,5.965929e-7,0.00003412705,0.003429167,0.9572365,0.001212156,0.0001074429,0.003256059],"study_design_scores_gemma":[0.0008040195,0.0001488638,0.7041809,0.0000694925,0.00006749442,0.00001698462,0.00002524342,0.08712211,0.2071145,0.0002509899,0.0001571739,0.00004221514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3483385,0.000006217046,0.6507151,0.0005748061,0.00003164983,0.000269489,0.00003088981,0.000009568463,0.00002376597],"genre_scores_gemma":[0.982555,0.0000197231,0.01727755,0.00003437526,0.00002129841,0.00002590504,0.000003775247,0.000008908812,0.00005343029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.750122,"threshold_uncertainty_score":0.1554918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517912310695694,"score_gpt":0.306189730629393,"score_spread":0.2810106075224361,"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."}}