{"id":"W3158917647","doi":"10.1016/j.pneurobio.2021.102055","title":"Determining laminar neuronal activity from BOLD fMRI using a generative model","year":2021,"lang":"en","type":"article","venue":"Progress in Neurobiology","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network","funders":"","keywords":"Laminar flow; Neuroscience; Premovement neuronal activity; SIGNAL (programming language); Laminar organization; Generative model; Voxel; Psychology; Physics; Computer science; Pattern recognition (psychology); Artificial intelligence; Generative grammar","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011462,0.0002553441,0.0003615953,0.0001064993,0.0002209806,0.00004622386,0.0002244026,0.0001165046,0.00002211141],"category_scores_gemma":[0.002365072,0.0002629107,0.00007960398,0.0003289684,0.0003872898,0.0002162825,0.000524879,0.0004348313,0.00001015626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007068313,"about_ca_system_score_gemma":0.0001666982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008120101,"about_ca_topic_score_gemma":0.00006582689,"domain_scores_codex":[0.997306,0.0007215562,0.0002323344,0.00111866,0.0001535063,0.000467977],"domain_scores_gemma":[0.996571,0.002869707,0.0001318943,0.0003156073,0.00005771723,0.00005405725],"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":[0.0001290239,0.0003357807,0.0436238,0.00001343835,0.00001473479,0.0004928719,0.0003205377,0.006614438,0.9405578,0.00073552,0.0000928712,0.007069144],"study_design_scores_gemma":[0.0007104421,0.00018621,0.02392752,0.00003018733,0.00001649243,0.0001276182,0.0000257571,0.2899743,0.6825598,0.001837372,0.0002363521,0.00036789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951485,0.0001588173,0.0009917363,0.002546088,0.000685702,0.0001722853,0.00006627291,0.00008241281,0.0001481338],"genre_scores_gemma":[0.9940166,0.00002996105,0.002614568,0.003054799,0.0001354941,0.00006436241,0.000004623083,0.00003096893,0.00004861254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2833599,"threshold_uncertainty_score":0.9999823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0945752901219176,"score_gpt":0.3222893262241951,"score_spread":0.2277140361022775,"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."}}