{"id":"W4385760990","doi":"10.1162/imag_a_00011","title":"RELIEF: A structured multivariate approach for removal of latent inter-scanner effects","year":2023,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health; University of Toronto","funders":"National Institute of Mental Health; Centre for Addiction and Mental Health Foundation; Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Scanner; Computer science; Generalizability theory; Harmonization; Univariate; Multivariate statistics; Artificial intelligence; Data mining; Machine learning; Context (archaeology); Data science; Statistics; Mathematics; Geography","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":[0.0001393899,0.0001203741,0.0001850787,0.0001476729,0.00009539037,0.00001736103,0.0002024341,0.00001794185,6.658498e-7],"category_scores_gemma":[0.0003751886,0.0001016265,0.00008133419,0.0006165281,0.0001895497,0.0000878519,0.0001100608,0.0001311409,0.000001524272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001891785,"about_ca_system_score_gemma":0.00003348948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009307189,"about_ca_topic_score_gemma":6.089302e-8,"domain_scores_codex":[0.9988936,0.0000169162,0.0001935974,0.0004492564,0.0001738409,0.0002728218],"domain_scores_gemma":[0.999272,0.00008210862,0.00009678,0.0003952762,0.00007440127,0.00007943812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003859474,0.00007085229,0.002524991,0.0001497755,0.00000162252,0.00004842394,0.00007374743,0.0002184206,0.9825705,0.000980531,0.001196632,0.01212595],"study_design_scores_gemma":[0.001974227,0.0003051387,0.127691,0.0002090272,0.00007864078,0.0008224611,0.00002071246,0.6265545,0.2159643,0.004057451,0.02193625,0.0003863054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364333,0.0000430069,0.8566584,0.002996163,0.0003916135,0.001834845,0.00003385529,0.0009906258,0.0006181887],"genre_scores_gemma":[0.9041364,0.00001196976,0.09431556,0.0008057033,0.00003766417,0.0001061207,0.00001112413,0.00002904373,0.0005464114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7677031,"threshold_uncertainty_score":0.4144207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05939705709650033,"score_gpt":0.3652103886801835,"score_spread":0.3058133315836832,"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."}}