{"id":"W4384025130","doi":"10.1038/s41597-023-02330-9","title":"Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration","year":2023,"lang":"en","type":"article","venue":"Scientific Data","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; University of British Columbia; McMaster University; Western University","funders":"Canada Foundation for Innovation; National Center for Research Resources; National Institute of Mental Health; NIH Blueprint for Neuroscience Research; National Institute on Aging; McDonnell Center for Systems Neuroscience; National Institutes of Health; Canada Research Chairs; Canada First Research Excellence Fund; Mitacs; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Fiducial marker; Neuroimaging; Computer science; Magnetic resonance imaging; Artificial intelligence; Image registration; Neuroanatomy; Computer vision; Medical physics; Medicine; Radiology; Image (mathematics); Anatomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009552619,0.0000607955,0.000115382,0.00004933627,0.0001850147,0.00007765416,0.0001959057,0.0000172274,0.000008579223],"category_scores_gemma":[0.0002663294,0.00004912089,0.000008939618,0.0002766122,0.0002444459,0.0001773398,0.00009670763,0.0000459983,0.000008108828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001083489,"about_ca_system_score_gemma":0.00006202952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001641919,"about_ca_topic_score_gemma":0.00004105714,"domain_scores_codex":[0.9989876,0.0000134782,0.0001626707,0.0005270728,0.0001543526,0.0001548354],"domain_scores_gemma":[0.9985038,0.00009214268,0.00005478222,0.00122922,0.00005601168,0.00006405383],"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":[0.0001917403,0.00007110769,0.001739047,0.0000762148,0.000002903785,0.000008844102,0.00003720163,0.000001528008,0.02348791,0.008822012,0.832383,0.1331784],"study_design_scores_gemma":[0.001380768,0.00005362366,0.02274226,0.00007543224,0.00005184619,0.00001963877,0.00009688143,0.02291966,0.0007787734,0.003684619,0.9480624,0.0001341346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05182827,0.00375892,0.662637,0.0399677,0.0005421711,0.008042047,0.2308789,0.001344729,0.001000259],"genre_scores_gemma":[0.6080889,0.0001439797,0.2317646,0.001082706,0.0002981329,0.0005263528,0.152409,0.00005839486,0.00562792],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5562606,"threshold_uncertainty_score":0.2003092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06958832490861831,"score_gpt":0.4077585015990662,"score_spread":0.3381701766904479,"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."}}