{"id":"W2951085789","doi":"10.3389/fnins.2019.00688","title":"LAB–QA2GO: A Free, Easy-to-Use Toolbox for the Quality Assessment of Magnetic Resonance Imaging Data","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; International Laboratory for Brain, Music and Sound Research","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft","keywords":"Toolbox; Magnetic resonance imaging; Quality (philosophy); Computer science; Nuclear magnetic resonance; Medicine; Physics; Radiology; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005302482,0.00009069106,0.000193938,0.00006741355,0.00006547646,0.00002361525,0.0009022557,0.00001959881,0.000004159098],"category_scores_gemma":[0.0006059686,0.00006869587,0.00003067484,0.0004812064,0.000165715,0.0002207127,0.0002953534,0.0001308414,5.740984e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000417686,"about_ca_system_score_gemma":0.00009483114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005341364,"about_ca_topic_score_gemma":0.000006509715,"domain_scores_codex":[0.9987186,0.00002683647,0.0002661123,0.0004893824,0.0002681949,0.0002308277],"domain_scores_gemma":[0.9978672,0.0001594506,0.00008896057,0.001764996,0.00006053419,0.00005888363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001517428,0.0003536741,0.7593963,0.000111952,0.000001193365,0.000005694629,0.0001244849,0.0004092526,0.07084498,0.01149535,0.05087202,0.1062334],"study_design_scores_gemma":[0.0005827517,0.0001448913,0.6330718,0.00006955151,0.00001207478,0.000004286636,0.00009969228,0.08237367,0.0006434559,0.0008164457,0.2820663,0.0001149745],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01405048,0.0005717129,0.9783669,0.003407564,0.0005192537,0.002183009,0.000296365,0.00005396109,0.0005507288],"genre_scores_gemma":[0.3854416,0.000209662,0.608963,0.003129495,0.00003077366,0.0002231328,0.00001242025,0.00002159305,0.00196831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3713911,"threshold_uncertainty_score":0.2801337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06527344623981103,"score_gpt":0.391195867108369,"score_spread":0.3259224208685579,"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."}}