{"id":"W2038429147","doi":"10.1002/hbm.21306","title":"Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance","year":2011,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; Canadian Institutes of Health Research","keywords":"Default mode network; Working memory; Task-positive network; Task (project management); Set (abstract data type); Resting state fMRI; Neuroscience; Cognition; Psychology; Dorsum; Functional connectivity; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001098483,0.0003158414,0.0003840272,0.0005112923,0.0009373872,0.00007971678,0.0003604089,0.0001236313,0.00006395226],"category_scores_gemma":[0.005902733,0.0003306529,0.00008717185,0.0008296692,0.0001608224,0.0004372312,0.0005758118,0.0005176699,0.00002834154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000299708,"about_ca_system_score_gemma":0.00003759043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005017164,"about_ca_topic_score_gemma":0.001685444,"domain_scores_codex":[0.9972007,0.0004301897,0.0004401438,0.0009247303,0.0003689606,0.0006352328],"domain_scores_gemma":[0.9965172,0.002667112,0.0002127146,0.0004336939,0.00005424097,0.0001150317],"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":[0.0002079578,0.0001822548,0.2759774,0.00007833359,0.00001698134,0.00008410766,0.006241458,0.004933679,0.7012763,0.0004066763,0.0006862594,0.009908563],"study_design_scores_gemma":[0.00115572,0.0001076005,0.9311644,0.0008887243,0.00000737065,0.00001425847,0.0007057492,0.0429833,0.02126698,0.0006152288,0.0004601795,0.0006304202],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854779,0.00003530706,0.01114689,0.000340769,0.0008675564,0.0005828586,0.000001165821,0.0001776833,0.001369859],"genre_scores_gemma":[0.9964628,0.000004075368,0.0005792195,0.002382665,0.0003840476,0.00005368592,0.000001557593,0.00004368012,0.00008833397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6800093,"threshold_uncertainty_score":0.9999145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1998743832204886,"score_gpt":0.3183839245999441,"score_spread":0.1185095413794555,"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."}}