The DOI matches its Crossref record; 120 of the 124 references it cites were found in a registry or an index.
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
Open accessPublished version of a preprint
Abstract
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
(2017) · Cited by 11,625
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Self-citation(2012) · Cited by 9,062
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Research works printed but not linked: 1
- Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, vol. 28, 6
Cited by
Sorted by citations
Items 201–271 of 271
Yeung, H. W., Stolicyn, A., Shen, X., Adams, M. J., Romaniuk, L., Thng, G., Buchanan, C. R., Tucker–Drob, E. M., Bastin, M. E., McIntosh, A. M., et al. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00064 · Cited by 2
Xia, J., Chen, N., & Qiu, A. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300577 · Cited by 2
Hwang, J., Kang, J.-E., Jeon, S., Lee, K. H., Kim, J.-W., & Lee, J. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.04.005 · Cited by 2
Comparing and scaling fMRI features for brain-behavior prediction
Schöttner, M., Bolton, T. A. W., Patel, J. H., & Hagmann, P. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.141 · Cited by 2
How feasible is end-to-end deep learning for clinical neuroimaging?
Farrugia, N. (2022) · Journal of Neuroradiology · doi:10.1016/j.neurad.2022.10.002 · Cited by 2
Zhao, B., Li, T., Li, Y., Fan, Z., Xiong, D., Wang, X., Gao, M., Smith, S. M., & Hongtu Zhu, B. (2022) · medRxiv · doi:10.1101/2022.02.22.22271371 · Cited by 2
Vieira, B. H., Schöttner, M., Calhoun, V. D., & Salmón, C. E. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.07.583858 · Cited by 5
Samardzija, A., Shen, X., Luo, W., Greene, A. S., Mehta, S., Tokoglu, F., Arora, J., Woods, S. W., Katz, R. R., Sanacora, G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.24.619737 · Cited by 2
Procrustes Alignment in Individual-level Analyses of Functional GradientsPreprint
Sasse, L., Paquola, C., Dukart, J., Hoffstaedter, F., Eickhoff, S. B., & Patil, K. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.26.625368 · Cited by 2
Self-citationEsmaeili, M., Bjørkeli, E. B., Pedersen, R., Falahati, F., Johansson, J. J., Nordin, K., Karalija, N., Bäckman, L., Nyberg, L. H., & Salami, A. (2025) · eLife · doi:10.7554/elife.104053.1 · Cited by 2
Buianova, I., Silvestrin, M., Deng, J. D., & Pornpattananangkul, N. (2025) · eLife · doi:10.7554/elife.108109 · Cited by 2
NeuroPredictome: A Data-Driven Predictome Linking Neuroimaging to Phenotype
Sultan, S. F., Mujica-Parodi, L. R., & Skiena, S. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669303 · Cited by 1
Zhang, Z., Wang, M., Lü, T., Shi, Y., Xie, C., Ren, Q., & Wang, Z. (2024) · Brain Communications · doi:10.1093/braincomms/fcaf033 · Cited by 1
Buianova, I., Silvestrin, M., Deng, J. D., & Pornpattananangkul, N. (2026) · eLife · doi:10.7554/elife.108109.3 · Cited by 2
Früh, D., Mendl-Heinisch, C., Bittner, N., Weis, S., & Caspers, S. (2025) · Human Brain Mapping · doi:10.1002/hbm.70191 · Cited by 2
SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity
Thapaliya, B., Akbaş, E., Sapkota, R., Ray, B., Calhoun, V. D., & Liu, J. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980889 · Cited by 1
Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis
Pope, I. Z., Chopra, S., Holmes, A., Francey, S. M., O'Donoghue, B. N., Cropley, V., Nelson, B., Yuen, H. P., Allott, K., Álvarez‐Jiménez, M., et al. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100589 · Cited by 1
Self-citationPredicting Real‐Life Cognitive Scores From Functional Connectivity
Kadushin, M., Madar, A., Tik, N., Bernstein‐Eliav, M., & Tavor, I. (2025) · Human Brain Mapping · doi:10.1002/hbm.70319 · Cited by 1
Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?
Muganga, T., Sasse, L., Larabi, D. I., Nieto, N., Caspers, J., Eickhoff, S. B., & Patil, K. R. (2025) · Human Brain Mapping · doi:10.1002/hbm.70323 · Cited by 1
Self-citationLi, T., Li, K., Zhao, Z., Sun, Q., Zhang, X., Yao, Z., Zhou, J., & Hu, B. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108151 · Cited by 2
Fang, X., Schwemmer, O., Hogan, A., & Marxen, M. (2026) · Biology of Sex Differences · doi:10.1186/s13293-026-00891-z · Cited by 1
Understanding Neurocognition With Deep Learning and MRI: A Systematic Review
Rahman, M. T., Orka, N. A., Khan, A., Lió, P., & Moni, M. A. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3635161 · Cited by 1
Pavlovich, K., Pang, J. C. S., & Fornito, A. (2025) · Network Neuroscience · doi:10.1162/netn.a.534 · Cited by 1
Detection of Alzheimer's disease using 3D-CNN model based on multimodal connectome
Dana, A. M. A., Karami, M., & Ghasemi, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114006 · Cited by 1
Data harmonizing via interpolation applied to brain age prediction
Nieto, N., Asati, A., Jadhav, K., & Patil, K. R. (2026) · Discover Data · doi:10.1007/s44248-026-00100-7 · Cited by 1
Study on MRI Slices-based Lightweight Neural Network in Alzheimer's Disease Detection
Zhang, Q., Long, Y., & Cai, H. (2023) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice58320.2023.10105740 · Cited by 1
Genon, S., & Li, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91688-2.00001-1 · Cited by 1
Gao, J., Zhao, L., Zhong, T., Li, C., He, Z., Wei, Y., Zhang, S., Guo, L., Liu, T., Han, J., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_27 · Cited by 1
Brain age monotonicity and functional connectivity differences of healthy subjects
Sorooshyari, S. K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300720 · Cited by 1
Inferring neurocognition using artificial intelligence on brain MRIs
Hussain, M. A., Grant, P. E., & Ou, Y. (2024) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2024.1455436 · Cited by 2
On some similarities and differences between deep neural networks and kernel learning machines
Pei, E., & Fokoué, E. (2022) · Mathematics for Applications · doi:10.13164/ma.2022.07 · Cited by 1
Nemali, A., Vockert, N., Berron, D., Maaß, A., Yakupov, R., Peters, O., Gref, D., Cosma, N. C., Preis, L., Priller, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484226 · Cited by 1
Chen, P., An, L., Wulan, N., Zhang, C., Zhang, S., Ooi, L. Q. R., Kong, R., Chen, J., Wu, J., Chopra, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.05.569848 · Cited by 1
Self-citationSex differences in the functional network underpinnings of psychotic-like experiences in childrenPreprint
Dhamala, E., Chopra, S., Ooi, L. Q. R., Rubio, J. M., Yeo, B. T. T., Malhotra, A. K., & Holmes, A. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.22.590660 · Cited by 2
Self-citationLearning diverse and generic representations of the brain with large-scale multi-task pretrainingPreprint
Leonardsen, E. H., Dahl, A., Holm, M. C., de Lange, A. G., Grødem, E., Marquand, A. F., Sørensen, Ø., Schwarz, E., Andreassen, O. A., Wolfers, T., et al. (2025) · medRxiv · doi:10.64898/2025.12.19.25342659 · Cited by 2
Sultan, S. F., Mujica-Parodi, L. R., & Skiena, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.07.415091
Graphs for deep learning representationsPreprint
Lassance, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.07439
Predictive Modeling Approaches
Barbey, A. K., Karama, S., & Haier, R. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.020
Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts
Yürekli, S., Demirtaş, M. A., & Rekik, I. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16919-9_14
3DCAE-MRI: Overcoming Data Availability in Small Sample Size MRI StudiesPreprint
Dias, M. D. F. M., Cerqueira, T. F. T., Duarte, J. V., Castelo-Branco, M. S., & Carvalho, P. (2023) · Research Square · doi:10.21203/rs.3.rs-3290143/v1 · Cited by 1
DouGNN: An End-to-End Deep Learning Framework for Predicting Individual Behaviors from fMRI Data
Cao, Q., & Wen, X. (2023) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml60161.2023.10424938
Supervised Phenotype Discovery from Multimodal Brain ImagingPreprint
Gong, W., Bai, S., Zheng, Y., Smith, S. M., & Beckmann, C. F. (2024) · Research Square · doi:10.21203/rs.3.rs-880321/v1
Dafflon, J., Moraczewski, D., Earl, E., Nielson, D. M., Loewinger, G., Mcclure, P., Thomas, A. G., & Pereira, F. (2024) · arXiv.org · doi:10.48550/arxiv.2405.00255
Generating Synthetic Task-based Brain Fingerprints for Population Neuroscience Using Deep LearningPreprint
Serin, E., Ritter, K., Schumann, G., Banaschewski, T., Marquand, A. F., & Walter, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.03.606469
Hussain, M. A., Grant, P. E., & Ou, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-74561-4_4
Esmaeili, M., Bjørkeli, E. B., Pedersen, R., Falahati, F., Johansson, J. J., Nordin, K., Karalija, N., Bäckman, L., Nyberg, L. H., & Salami, A. (2025) · eLife · doi:10.1101/2024.11.19.624334
Connectome-based prediction of individual behaviors via convolutional graph propagation network
Wu, D., & Li, X. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781694
Voxel-wise or Region-wise Nuisance Regression for Functional Connectivity Analyses: Does it matter?Preprint
Muganga, T., Sasse, L., Larabi, D. I., Nieto, N., Caspers, J., Eickhoff, S. B., & Patil, K. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.10.627766
Self-citationZalesky, A., Sotiropoulos, S. N., Jbabdi, S., & Fornito, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00007-0
Huang, Z., & Xiao, L. (2025) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3724979.3725004
A Machine Learning Application for Biological Gender Prediction Based on Patient Records
Çoban, Ö., & Altay, Ş. Y. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-97992-7_20
A Connectivity-Enhanced Concatenated Dynamic Graph Neural Network for Cognitive Ability Prediction
Zhuang, Y., Su, J., Fan, Z., Yu, Y., Zeng, L., & Hu, D. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090245
Buianova, I., Silvestrin, M., Deng, J. D., & Pornpattananangkul, N. (2025) · eLife · doi:10.7554/elife.108109.1
Riley, S., Cheng, A., Wang, Y.-W., Shen, X.-L., Dhamala, E., Zhao, Y.-Z., Holmes, A. J., Constable, R., & Yip, S. W. (2026) · Imaging neuroscience · doi:10.1162/imag.a.1287
Self-citationCheng, C., Li, Y., Wen, J., Chen, X., & Liu, A. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254345
Transfer Learning for Improving Neuroimaging-Based Diagnostic Classification
Deshpande, G., Lu, B., Huynh, N., & Rangaprakash, D. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3647485
Brain age group classification based on resting state functional connectivity metrics
Singh, P., Yadav, K. S., Kumar, L., & Gandhi, T. K. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109617
Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping
Gao, K., Wang, L., Li, L., Chen, X., Li, B., Wang, Y.-W., Li, X.-Y., Wang, Z.-H., Li, H.-X., Liao, Y., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103949
Buianova, I., Silvestrin, M., Deng, J. D., & Pat, N. (2026) · eLife · doi:10.7554/elife.108109.2
Starčević, A., & Maliković, A. (2026) · Medicinska istrazivanja · doi:10.5937/medi0-64446
Graphs for deep learning representations
Lassance, C. (2020) · theses.fr (ABES) · doi:10.70675/71e4bd29z1d6az4a2bza3c2z868925d41708
Sabbagh, D. (2021) · theses.fr (ABES) · doi:10.70675/b70e0626zdd29z45e9z9dc4zfc919b40c191 · Cited by 1
Lei, C., Xu, X., Yang, H., Du, J., Sheng, L., Li, X., Han, Y., Cheng, X., Yang, Y. F., Wang, C., et al. (2026) · Schizophrenia Research · doi:10.1016/j.schres.2026.04.014
Giri, P., Deepana, S., Karishma, P., P, D. G., Venkatesh, S., & Amirtharajan, R. (2026) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide69088.2026.11545113
Dong, C., Gu, Z., Li, D., Xi, X., Luo, B., & Sun, D. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133360
Fang, P., Li, Y. M., Zhu, S., Bi, K., Li, Y. M., Yu, J., Zhang, J., Zheng, M., Zhu, J., & Zhu, D. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08326-0
Fields, C. T., Rosenblatt, M., Aina, J., Thind, J. K., Harper, A., Bellamy, C. D., Zhou, X., Potter, A., Garavan, H., Allgaier, N. A., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9557211/v1
Gillig, A., Jobard, G., Cremona, S., & Joliot, M. (2026) · Network Neuroscience · doi:10.1162/netn.a.600
Exploring Healthy Neurocognitive Ageing with Deep Learning Interpretability MethodsPreprint
Sénant, Q., Guichet, C., Grivel, N., Baciu, M. V., & Mermillod, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.14.751405
Short-To-Long Functional Connectivity Transfer via Structure-Aware Latent Diffusion
Torres, B., Liu, Y., & Acharyya, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37242-0_6
Effect sizes for brain-based predictive models of cognition across the lifespan
Christensen, E., Wiersch, L., Wang, M., McPherson, B., Shearer, H., Noble, S., Lam, K., Poline, J., & Dhamala, E. (2026) · Communications Psychology · doi:10.1038/s44271-026-00528-2
Metadata
- Authors
- He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T.
- Affiliations
- National University of Singapore (SG)
- Yale University (US)
- Cornell University (US)
- Forschungszentrum Jülich (DE)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Yeo, B.T. Thomas
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 206
- Pages
- 116276
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6984975
- OpenAlex
- W2979650665
- Cited by
- 271
- References
- 124
- Citations by year
- 2026: 19 · 2025: 46 · 2024: 46 · 2023: 41 · 2022: 47 · 2021: 42 · 2020: 26
- FWCI
- 17.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, cc-by-nc-nd)
- National University of Singapore (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Medical Research Council (CBRG/0088/2015)
- National University of Singapore (R185000271720, DPRT/944/09/14)
- National Institutes of Health
- Ministry of Education - Singapore (MOE2014-T2-2-016)
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (1U54MH091657, 25163)
- National Research Foundation Singapore
- NIH Blueprint for Neuroscience Research
- McDonnell Center for Systems Neuroscience
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- deep neural network, kernel regression, resting-state functional connectivity, feedforward neural networks, behavior prediction, fluid intelligence, demographic prediction, sample size effects, computational cost, Human Connectome Project, UK Biobank
- MeSH terms
- Machine Learning, Deep Learning, Adult, Age Factors, Aged, Brain, Female, Humans, Image Interpretation, Computer-Assisted, Intelligence, Magnetic Resonance Imaging, Male, Middle Aged, Psychomotor Performance, Sex Factors, Neural Networks, Computer, Biological Specimen Banks, Young Adult, Connectome, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
