{"id":"W2952906091","doi":"10.1038/sdata.2014.54","title":"A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures","year":2015,"lang":"en","type":"article","venue":"Scientific Data","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Max-Planck-Gesellschaft","keywords":"Resting state fMRI; Functional magnetic resonance imaging; Cognition; Prefrontal cortex; Session (web analytics); Psychology; Audiology; Computer science; Neuroscience; Cognitive psychology; 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":[0.001494592,0.0009778659,0.0009730625,0.001330537,0.0007894923,0.0006596275,0.001576872,0.001110474,0.009084363],"category_scores_gemma":[0.004948049,0.000408768,0.0007986611,0.001448364,0.0004065669,0.0004693535,0.0009896006,0.0007236297,0.00865532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004415071,"about_ca_system_score_gemma":0.001027786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007132502,"about_ca_topic_score_gemma":0.02220006,"domain_scores_codex":[0.9993026,0.000131334,0.0001016348,0.0002342899,0.0001687138,0.0000614631],"domain_scores_gemma":[0.9966287,0.0006870827,0.0002425507,0.001039696,0.001247666,0.0001542216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.003502708,0.001544164,0.06501072,0.003894139,0.001584377,0.001694412,0.001233452,0.005075644,0.1038244,0.001555033,0.633806,0.1772749],"study_design_scores_gemma":[0.0007779414,0.0009632697,0.597914,0.0003118971,0.0007849411,0.004513822,0.0003797411,0.003820982,0.0184764,0.004679493,0.3670163,0.0003611819],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.112212,0.0009615976,0.02364609,0.0005176548,0.0002616915,0.001575565,0.850136,0.003187347,0.007502047],"genre_scores_gemma":[0.0659999,0.0002333395,0.02162369,0.0002471575,0.0001195511,0.004070058,0.9024774,0.000737311,0.004491561],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009084363,"threshold_uncertainty_score":0.03039026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2342273660817456,"score_gpt":0.3202344923102148,"score_spread":0.08600712622846918,"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."}}