{"id":"W4282928084","doi":"10.1038/s41597-022-01386-3","title":"A longitudinal multi-scanner multimodal human neuroimaging dataset","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute of Mental Health; U.S. Department of Health and Human Services","keywords":"Neuroimaging; Scanner; Modalities; Human brain; Computer science; Diffusion MRI; Consistency (knowledge bases); Artificial intelligence; Medical physics; Psychology; Medicine; Neuroscience; Magnetic resonance imaging; Radiology","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":[0.001660153,0.001188686,0.00103454,0.001528953,0.0008440181,0.001194183,0.002113836,0.002201965,0.01337542],"category_scores_gemma":[0.004230507,0.000560085,0.001221002,0.001864071,0.0005143944,0.0006848808,0.001557038,0.001524407,0.01288453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009489575,"about_ca_system_score_gemma":0.001409356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01292474,"about_ca_topic_score_gemma":0.0296895,"domain_scores_codex":[0.999168,0.0001926498,0.00009622291,0.0002862477,0.0001594975,0.000097393],"domain_scores_gemma":[0.9977965,0.0004615123,0.0002377618,0.0008291969,0.000505669,0.0001693929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001623376,0.0007946682,0.03292893,0.001551055,0.00106953,0.001667213,0.0003352106,0.005401814,0.005635179,0.002075806,0.8898722,0.05704498],"study_design_scores_gemma":[0.001164505,0.000809836,0.17796,0.0006812223,0.0006333554,0.005772388,0.0005828993,0.01035246,0.005834279,0.01070726,0.785065,0.0004368765],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03126082,0.0009025889,0.005825671,0.001040119,0.0001809185,0.0002949454,0.9562364,0.001597774,0.002660794],"genre_scores_gemma":[0.02547374,0.0002892486,0.005348766,0.0002944547,0.00007856966,0.0008364926,0.9650218,0.0001837764,0.002473108],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01337542,"threshold_uncertainty_score":0.04474527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2147125517618612,"score_gpt":0.3618615470727076,"score_spread":0.1471489953108464,"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."}}