{"id":"W2093738571","doi":"10.1016/j.neuroimage.2011.08.021","title":"PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; Baycrest Hospital","funders":"","keywords":"Communication noise; Noise (video); Computer science; Pattern recognition (psychology); Artificial intelligence; Resampling; Autocorrelation; Artifact (error); Noise reduction; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002440828,0.001341283,0.0008338117,0.001065324,0.0006308898,0.001502041,0.001572314,0.001338311,0.005014141],"category_scores_gemma":[0.00822632,0.001056592,0.0007607595,0.001078829,0.000627325,0.001415993,0.001735087,0.001522836,0.001948899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000306777,"about_ca_system_score_gemma":0.001364621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001560332,"about_ca_topic_score_gemma":0.004410547,"domain_scores_codex":[0.999118,0.0002190038,0.00004378998,0.0002388677,0.0003157669,0.00006450003],"domain_scores_gemma":[0.9982711,0.0008310261,0.0001288927,0.0003825965,0.0003078365,0.00007851839],"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":[0.001397579,0.0004064546,0.003719513,0.001062508,0.0004834067,0.0004322554,0.0003008394,0.02270207,0.3967235,0.0109774,0.02022892,0.5415655],"study_design_scores_gemma":[0.0002749439,0.0003746643,0.01536809,0.00006585614,0.0002314363,0.001245571,0.00005373673,0.6250054,0.3194365,0.01210306,0.02561669,0.0002240774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01069798,0.0001312246,0.980682,0.00009497518,0.00009555118,0.0001539889,0.0006921638,0.006906038,0.0005460201],"genre_scores_gemma":[0.07472061,0.0002224146,0.9182096,0.0001755579,0.00008992345,0.0008441056,0.001208943,0.002731132,0.00179763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005014141,"threshold_uncertainty_score":0.016774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2669906976494517,"score_gpt":0.2962755932850887,"score_spread":0.02928489563563696,"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."}}