{"id":"W2891483161","doi":"10.1186/s12880-018-0266-4","title":"Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study","year":2018,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; NeuroRx Research (Canada)","funders":"Canadian Institutes of Health Research; National Institute on Aging; Alzheimer's Disease Neuroimaging Initiative","keywords":"Image quality; Computer science; Artificial intelligence; Metric (unit); Feature (linguistics); Contrast (vision); Quality Score; Distortion (music); Quality (philosophy); Pattern recognition (psychology); Protocol (science); Neuroimaging; Grayscale; Similarity (geometry); Standardization; Computer vision; Data mining; Image (mathematics); Medicine; Pathology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005001734,0.0003026087,0.000549219,0.0003564053,0.0002589873,0.0004415427,0.001366067,0.00004951108,0.00001914997],"category_scores_gemma":[0.002875165,0.000265724,0.0001371674,0.0008837073,0.0002312057,0.0008059844,0.0006394534,0.0003198796,0.00001622909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002218968,"about_ca_system_score_gemma":0.0003882298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000270514,"about_ca_topic_score_gemma":0.000190321,"domain_scores_codex":[0.9946032,0.00132191,0.0009885974,0.000984322,0.001316288,0.0007857435],"domain_scores_gemma":[0.9969049,0.001481786,0.0001977442,0.0008579045,0.0002874691,0.0002701897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002756685,0.001376613,0.6437999,0.0009111801,0.00003215447,0.0004890647,0.006476759,0.00000599299,0.001110861,0.003435899,0.004000128,0.3380858],"study_design_scores_gemma":[0.02338005,0.0003551981,0.5628939,0.000552006,0.00002767996,0.0001158543,0.004196115,0.4036996,0.001178751,0.0003765508,0.002270329,0.0009539594],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08312602,0.0009677848,0.9118269,0.001686721,0.0009845858,0.0009846729,0.00001889096,0.0002629656,0.0001414757],"genre_scores_gemma":[0.9231344,0.000003383111,0.07525583,0.001189118,0.0002263792,0.00009286038,0.000003641085,0.0000255124,0.00006888874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8400084,"threshold_uncertainty_score":0.9999795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05769380088286784,"score_gpt":0.40249826487827,"score_spread":0.3448044639954021,"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."}}