{"id":"W4282840939","doi":"10.1101/2022.06.11.495739","title":"To Smooth or not to Smooth: Enhancing Specificity While Maintaining Sensitivity","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Alexander von Humboldt-Stiftung; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; University of Southern California; F. Hoffmann-La Roche; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Horizon 2020 Framework Programme; Foundation for the National Institutes of Health","keywords":"Smoothing; Voxel; Kernel (algebra); Neuroimaging; Computer science; Gaussian blur; Pattern recognition (psychology); Artificial intelligence; Kernel smoother; Mathematics; Kernel method; Neuroscience; Computer vision; Psychology; Image processing; Support vector machine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00923601,0.001144737,0.001268792,0.001565051,0.0006934861,0.002548468,0.0008980775,0.001453478,0.003359067],"category_scores_gemma":[0.03994163,0.0007409189,0.000936178,0.001273273,0.001579501,0.001911189,0.001913028,0.001677889,0.00126158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003512242,"about_ca_system_score_gemma":0.0009352614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004233,"about_ca_topic_score_gemma":0.001629808,"domain_scores_codex":[0.9957048,0.001818066,0.0003784016,0.001209479,0.0005766713,0.0003126207],"domain_scores_gemma":[0.9819228,0.0104311,0.00122387,0.003909729,0.002063873,0.0004485311],"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":[0.00434534,0.0005820844,0.05584888,0.001814903,0.002549023,0.00098574,0.001522148,0.02964066,0.4818634,0.01538641,0.007709986,0.3977514],"study_design_scores_gemma":[0.0003847424,0.001678388,0.1271959,0.0004406459,0.002254922,0.003445099,0.000779091,0.3229863,0.4542603,0.06597069,0.02008047,0.0005234991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4331103,0.001790288,0.5548428,0.001469013,0.0002715006,0.0001914442,0.0003604387,0.003654789,0.004309399],"genre_scores_gemma":[0.7869502,0.0004798207,0.2087733,0.0005215989,0.0001085421,0.0001199576,0.0003445941,0.001255674,0.001446238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00923601,"threshold_uncertainty_score":0.04884529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04600699397822091,"score_gpt":0.2624204674012321,"score_spread":0.2164134734230112,"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."}}