{"id":"W2737036004","doi":"10.3389/fnhum.2017.00404","title":"Differing Time of Onset of Concurrent TMS-fMRI during Associative Memory Encoding: A Measure of Dynamic Connectivity","year":2017,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McGill University; Douglas Mental Health University Institute; Centre for Addiction and Mental Health","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Associative property; Measure (data warehouse); Content-addressable memory; Neuroscience; Encoding (memory); Psychology; Computer science; Artificial intelligence; Data mining; Mathematics; Artificial neural network","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":[0.0001490659,0.0001782946,0.0001565235,0.0002926094,0.00008600368,0.0001656392,0.0001283915,0.000329696,0.001237402],"category_scores_gemma":[0.0009866435,0.0001107741,0.00006659702,0.000165227,0.0002028415,0.000252865,0.0001553398,0.0002390312,0.000100056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001135462,"about_ca_system_score_gemma":0.00007005792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005357241,"about_ca_topic_score_gemma":0.001516908,"domain_scores_codex":[0.9999335,0.0000114837,0.000004714248,0.00002517088,0.00001535578,0.000009756186],"domain_scores_gemma":[0.9997712,0.0001188412,0.00004818802,0.00001284154,0.00002278602,0.00002620809],"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":[0.001069926,0.0000740508,0.01073026,0.00006305153,0.0000268603,0.00009986449,0.0001717182,0.0001799056,0.97409,0.00008542832,0.00004288966,0.01336612],"study_design_scores_gemma":[0.00009620059,0.002007626,0.8333827,0.00001198953,0.0001134219,0.0009618927,0.000172919,0.002546341,0.1595724,0.0005491084,0.0005637034,0.00002173664],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965531,0.0001135922,0.002674286,0.00001901466,0.000004821577,0.00002013479,0.00006638967,0.00001513826,0.0005335387],"genre_scores_gemma":[0.9976953,0.00006548112,0.001794801,0.00001884821,0.00000923812,0.00002931045,0.00008335379,0.000006805256,0.0002968549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001237402,"threshold_uncertainty_score":0.004139483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03634282222668202,"score_gpt":0.2838125764893128,"score_spread":0.2474697542626308,"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."}}