{"id":"W2110692145","doi":"10.1016/j.mri.2006.09.042","title":"CORSICA: correction of structured noise in fMRI by automatic identification of ICA components","year":2006,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Independent component analysis; Artificial intelligence; Noise (video); Computer science; Pattern recognition (psychology); Functional magnetic resonance imaging; Noise reduction; Aliasing; Communication noise; Brain activity and meditation; Prior probability; Electroencephalography; Bayesian probability; Undersampling; Image (mathematics); Neuroscience","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":[0.0003152029,0.0001045312,0.0001909582,0.0001968908,0.0000306335,0.00005076074,0.0004127046,0.00003356989,0.00001193418],"category_scores_gemma":[0.00004028165,0.0001132706,0.00003643729,0.0005130505,0.00008820742,0.000309646,0.00006785453,0.00009587633,0.000002976501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004276025,"about_ca_system_score_gemma":0.000027275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004475067,"about_ca_topic_score_gemma":0.00001817109,"domain_scores_codex":[0.9985725,0.00011094,0.0006068421,0.000259084,0.0003007169,0.0001499565],"domain_scores_gemma":[0.9990987,0.00006705039,0.0003184615,0.0003976529,0.00009917662,0.00001902111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001367445,0.0002656809,0.05713211,0.00006707838,0.000001724748,0.000003632671,0.0009748087,0.000461292,0.5142962,0.003873965,0.00798538,0.4149245],"study_design_scores_gemma":[0.0002912702,0.00002663374,0.4622861,0.00006818824,0.00000294764,0.000004282691,0.00001607364,0.4411546,0.09290563,0.002255703,0.0008852227,0.0001033833],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7878653,0.001703236,0.2079401,0.0005252933,0.000297545,0.000418629,0.000007794612,0.0001722485,0.001069805],"genre_scores_gemma":[0.9887744,0.00001250171,0.01088487,0.0000433609,0.000007917158,0.00001788688,0.000009631687,0.000007642875,0.000241747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4406933,"threshold_uncertainty_score":0.4619043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005566741325366598,"score_gpt":0.2297228262656513,"score_spread":0.2241560849402847,"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."}}