{"id":"W4225553011","doi":"10.1007/s11682-022-00650-9","title":"Alternative labeling tool: a minimal algorithm for denoising single-subject resting-state fMRI data with ICA-MELODIC","year":2022,"lang":"en","type":"article","venue":"Brain Imaging and Behavior","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"National Institute of Mental Health; National Institutes of Health; Canadian Institutes of Health Research; Centre for Addiction and Mental Health Foundation","keywords":"Independent component analysis; Computer science; Resting state fMRI; Pattern recognition (psychology); Functional magnetic resonance imaging; Artificial intelligence; Noise reduction; Skew; Noise (video); Psychology; Neuroscience; Image (mathematics)","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007620454,0.000250369,0.0002448954,0.0001704923,0.00142711,0.0002246137,0.0004264508,0.00001450718,0.00001106246],"category_scores_gemma":[0.003333479,0.0002422111,0.0000410284,0.0003465336,0.0002601028,0.0004413318,0.0008651784,0.0002928954,0.000001917997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111,"about_ca_system_score_gemma":0.00009247156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001372139,"about_ca_topic_score_gemma":0.00002855955,"domain_scores_codex":[0.9974753,0.000198109,0.0002536902,0.001124183,0.0004778441,0.000470877],"domain_scores_gemma":[0.994615,0.004626128,0.0001715229,0.0004432969,0.0000751538,0.00006894788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003495631,0.0008143079,0.009957321,0.00005999044,0.0000416894,0.0006828711,0.002150531,0.000319488,0.3499987,0.00008323373,0.007083395,0.6284589],"study_design_scores_gemma":[0.0287417,0.008153349,0.03224989,0.001136607,0.002048303,0.01120322,0.01283657,0.4919227,0.2991149,0.003940965,0.1001641,0.008487663],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729655,0.0003182346,0.01789035,0.006026376,0.0005717842,0.000878083,0.0009931367,0.000268779,0.00008770046],"genre_scores_gemma":[0.959531,0.00001018914,0.03381328,0.004819827,0.0002902935,0.0005500629,0.00007479726,0.0001136209,0.0007968901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6199713,"threshold_uncertainty_score":0.9998729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07047080259261813,"score_gpt":0.30438537036102,"score_spread":0.2339145677684019,"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."}}