Advancing Fusion with Machine Learning Research Needs Workshop Report
Open accessAbstract
Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML can help accelerate fusion research by guiding experimental design and monitoring physical processes.
“ML can help accelerate this work by, e.g., guiding experimental design and monitoring physical processes; see also [360].”
From Tackling climate change with machine learning (2023) · cites this paper as [360]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
(2017) · Cited by 11,625 · see citing works
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070 · see citing works
Soft actor-critic Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1801.01290 · Cited by 5,594 · see citing works
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517384113 · Cited by 4,796 · see citing works
Bayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 3,974 · see citing works
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2023) · Figshare · doi:10.6084/m9.figshare.5203696.v2 · Cited by 2,088 · see citing works
Uncertainty Quantification: Theory, Implementation, and Applications
Smith, R. C. (2013) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611973228 · Cited by 1,179 · see citing works
fPINNs: Fractional Physics-Informed Neural Networks
Pang, G., Lu, L., & Karniadakis, G. E. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1229845 · Cited by 1,105 · see citing works
Zhu, Y., Zabaras, N. J., Koutsourelakis, P., & Perdikaris, P. G. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2019.05.024 · Cited by 1,029 · see citing works
Hansen, C. D., & Johnson, C. R. (2005) · Cited by 950 · see citing works
Computer Model Calibration Using High-Dimensional Output
Higdon, D., Gattiker, J., Williams, B. J., et al. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000000888 · Cited by 892 · see citing works
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature ModelingPreprint
Daw, A., Karpatne, A., Watkins, W., Read, J., & Kumar, V. (2017) · arXiv.org · doi:10.48550/arxiv.1710.11431 · Cited by 695 · see citing works
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., & Shah, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793742 · Cited by 694 · cited 1× · see citing works
“In each case, a complex dynamical system with limited models and complex external influences has benefited from a variety of ML-based controller development [2, 12, 21, 35, 45, 65, 71].”
BackgroundAn Application of Reinforcement Learning to Aerobatic Helicopter Flight
Abbeel, P., Coates, A., Quigley, M., & Ng, A. Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0006 · Cited by 551 · see citing works
The genome of the offspring of a Neanderthal mother and a Denisovan father
Slon, V., Mafessoni, F., Vernot, B., de Filippo, C., Grote, S., Viola, B., Hajdinjak, M., Peyrégne, S., Nagel, S., Brown, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0455-x · Cited by 536 · see citing works
PDE-Net: Learning PDEs from DataPreprint
Long, Z., Lu, Y., Ma, X., & Dong, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.09668 · Cited by 507 · see citing works
Disruptions in ITER and strategies for their control and mitigation
Lehnen, M., Aleynikova, K., Aleynikov, P., Campbell, D. J., Drewelow, P., Eidietis, N. W., Gasparyan, Y., Granetz, R., Gribov, Y., Hartmann, N., et al. (2014) · Journal of Nuclear Materials · doi:10.1016/j.jnucmat.2014.10.075 · Cited by 426 · see citing works
Self-citationPredicting disruptive instabilities in controlled fusion plasmas through deep learning
Kates-Harbeck, J., Svyatkovskiy, A., & Tang, W. (2019) · Nature · doi:10.1038/s41586-019-1116-4 · Cited by 367 · see citing works
Exascale Deep Learning for Climate Analytics
Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips, E. H., Mahesh, A., Matheson, M. A., Deslippe, J. R., Fatica, M., et al. (2018) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2018.00054 · Cited by 297 · see citing works
Capturing chemical intuition in synthesis of metal-organic frameworks
Moosavi, S. M., Chidambaram, A., Talirz, L., Haranczyk, M., Stylianou, K. C., & Smit, B. (2019) · Nature Communications · doi:10.1038/s41467-019-08483-9 · Cited by 257 · see citing works
Learning for control from multiple demonstrations
Coates, A., Abbeel, P., & Ng, A. Y. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390175 · Cited by 228 · see citing works
Proper orthogonal decomposition for reduced basis feedback controllers for parabolic equations
Atwell, J. A., & King, B. B. (2001) · Mathematical and Computer Modelling · doi:10.1016/s0895-7177(00)00225-9 · Cited by 225 · see citing works
On generalized moving least squares and diffuse derivatives
Mirzaei, D., Schaback, R., & Dehghan, M. (2011) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/drr030 · Cited by 217 · see citing works
Data Management Challenges in Production Machine Learning
Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2017) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3035918.3054782 · Cited by 214 · see citing works
Data Lifecycle Challenges in Production Machine Learning: A Survey
Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2018) · ACM SIGMOD Record · doi:10.1145/3299887.3299891 · Cited by 214 · see citing works
Chapter 8: Plasma operation and control
Gribov, Y., Humphreys, D. A., KAJIWARA, K., et al. (2007) · Nuclear Fusion · doi:10.1088/0029-5515/47/6/s08 · Cited by 182 · see citing works
Self-citationDesign and first applications of the ITER integrated modelling & analysis suite
Imbeaux, F., Pinches, S. D., Lister, J. B., et al. (2015) · Nuclear Fusion · doi:10.1088/0029-5515/55/12/123006 · Cited by 172 · see citing works
Data-driven discovery of PDEs in complex datasets
Berg, J., & Nyström, K. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2019.01.036 · Cited by 160 · see citing works
Tripled yield in direct-drive laser fusion through statistical modelling
Gopalaswamy, V., Betti, R., Knauer, J. P., et al. (2019) · Nature · doi:10.1038/s41586-019-0877-0 · Cited by 160 · see citing works
Three pitfalls to avoid in machine learning.
Riley, P. (2019) · Nature · doi:10.1038/d41586-019-02307-y · Cited by 142 · see citing works
Tokamak disruption alarm based on a neural network model of the high- beta limit
Wroblewski, D., Jahns, G. L., & Leuer, J. A. (1997) · Nuclear Fusion · doi:10.1088/0029-5515/37/6/i02 · Cited by 135 · see citing works
Solving differential equations using deep neural networks
Michoski, C., Milosavljević, M., Oliver, T., & Hatch, D. R. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.015 · Cited by 127 · see citing works
Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod
Rea, C., Granetz, R., Montes, K., Tinguely, R. A., Eidietis, N. W., Hanson, J. M., & Sammuli, B. (2018) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/aac7fe · Cited by 111 · see citing works
Self-citationMachine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST
Montes, K., Rea, C., Granetz, R., Tinguely, R. A., Eidietis, N. W., Meneghini, O., Chen, D., Shen, B., Xiao, B., Erickson, K. G., et al. (2019) · Nuclear Fusion · doi:10.1088/1741-4326/ab1df4 · Cited by 105 · see citing works
Self-citationStructure Preserving Model Reduction of Parametric Hamiltonian Systems
Afkham, B. M., & Hesthaven, J. S. (2017) · SIAM Journal on Scientific Computing · doi:10.1137/17m1111991 · Cited by 100 · see citing works
A cross-tokamak neural network disruption predictor for the JET and ASDEX Upgrade tokamaks
Windsor, C. G., Pautasso, G., Tichmann, C., Buttery, R. J., Hender, T. C., Contributors, J. E., & Team, T. A. U. (2005) · Nuclear Fusion · doi:10.1088/0029-5515/45/5/004 · Cited by 91 · see citing works
Zonal flow generation in inertial confinement fusion implosions
Peterson, J. L., Humbird, K., Field, J. E., et al. (2017) · Physics of Plasmas · doi:10.1063/1.4977912 · Cited by 82 · see citing works
Making inertial confinement fusion models more predictive
Gaffney, J., Brandon, S. T., Humbird, K., Kruse, M., Nora, R., Peterson, J. L., & Spears, B. K. (2019) · Physics of Plasmas · doi:10.1063/1.5108667 · Cited by 75 · see citing works
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural NetworksPreprint
Tartakovsky, A. M., Marrero, C. O., Perdikaris, P. G., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.03398 · Cited by 74 · see citing works
Large Scale Bayesian Data Analysis for Nuclear Fusion Experiments
Svensson, J., & Werner, A. T. (2007) · IEEE International Symposium on Intelligent Signal Processing, · doi:10.1109/wisp.2007.4447579 · Cited by 72 · see citing works
Data-driven material models for atomistic simulation
Wood, M. A., Cusentino, M. A., Wirth, B. D., & Thompson, A. P. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.99.184305 · Cited by 68 · see citing works
Machine learning control for disruption and tearing mode avoidance
Fu, Y., Eldon, D., Erickson, K. G., Kleijwegt, K., Lupin-Jimenez, L. C., Boyer, M. D., Eidietis, N. W., Barbour, N., Izacard, O., & Kolemen, E. (2020) · Physics of Plasmas · doi:10.1063/1.5125581 · Cited by 68 · see citing works
Self-citationActive Control of Magneto-hydrodynamic Instabilities in Hot Plasmas
Igochine, V. (2014) · Springer series on atomic, optical, and plasma physics · doi:10.1007/978-3-662-44222-7 · Cited by 64 · see citing works
Novel aspects of plasma control in ITER
Humphreys, D. A., Ambrosino, G., de Vries, P. C., et al. (2015) · Physics of Plasmas · doi:10.1063/1.4907901 · Cited by 63 · see citing works
Self-citationAchievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm
Baltz, E. A., Trask, E., Binderbauer, M. W., Dikovsky, M., Hiroshi, G. O. T. A., Mendoza, R., Platt, J. C., & Riley, P. F. (2017) · Scientific Reports · doi:10.1038/s41598-017-06645-7 · Cited by 45 · see citing works
A reduced resistive wall mode kinetic stability model for disruption forecasting
Berkery, J. W., Sabbagh, S. A., Bell, R. E., et al. (2017) · Physics of Plasmas · doi:10.1063/1.4977464 · Cited by 44 · see citing works
Three-dimensional equilibrium reconstruction on the DIII-D device
Lazerson, S., & the DIII-D Team (2015) · Nuclear Fusion · doi:10.1088/0029-5515/55/2/023009 · Cited by 33 · see citing works
Expensive Function Optimization with Stochastic Binary Outcomes
Tesch, M., Schneider, J., & Choset, H. (2018) · Figshare · doi:10.1184/r1/6554789 · Cited by 23 · see citing works
Helical core reconstruction of a DIII-D hybrid scenario tokamak discharge
Cianciosa, M. R., Wingen, A., Hirshman, S. P., et al. (2017) · Nuclear Fusion · doi:10.1088/1741-4326/aa6f82 · Cited by 22 · see citing works
Efficient I/O and Storage of Adaptive-Resolution Data
Kumar, S., Edwards, J., Bremer, P., Knoll, A. M., Christensen, C., Vishwanath, V., Carns, P., Schmidt, J. A., & Pascucci, V. (2014) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2014.39 · Cited by 21 · see citing works
Self-citationGlasser, A. S., Kolemen, E., & Glasser, A. H. (2018) · Physics of Plasmas · doi:10.1063/1.5007042 · Cited by 20 · see citing works
Self-citationOsthus, D., Wiel, S. V., Hoffman, N. M., & Wysocki, F. J. (2019) · SIAM/ASA Journal on Uncertainty Quantification · doi:10.1137/17m1158860 · Cited by 13 · see citing works
ICEE: Wide-area In Transit Data Processing Framework For Near Real-Time Scientific Applications
Choi, J. Y., Wu, K. J., Jacky, W., et al. (2013) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 11 · see citing works
Solving Irregular and Data-enriched Differential Equations using Deep Neural NetworksPreprint
Michoski, C., Milosavljević, M., Oliver, T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04351 · Cited by 9 · see citing works
Seal, S. K., Cianciosa, M. R., Hirshman, S. P., et al. (2017) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2017.37 · Cited by 8 · see citing works
Scalable Visualization and Interactive Analysis Using Massive Data Streams
Pascucci, V., Bremer, P., Gyulassy, A. G., et al. (2013) · Advances in parallel computing · doi:10.3233/978-1-61499-322-3-212 · Cited by 5 · see citing works
Self-citationNonlinear integro-differential operator regression with neural networksPreprint
Patel, R. G., & Desjardins, O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08552
Flagged first, then by citations
Research works printed but not linked: 10
- I. Goodfellow et al., Generative adversarial nets, in Advances in Neural Information Processing Systems (2014)
- V. Igochine, Active Control of Magnetohydrodynamic Instabili- ties in Hot Plasmas. Springer Series on Atomic, Optical, and Plasma Physics (Springer, Berlin, 2015). ISBN 978-3-662- 44221-0
- R. Iten, T. Metger, H. Wilming, L. del Rio, R. Renner, Discov- ering Physical Concepts with Neural Networks (2018), arXiv: 1807.10300 · arXiv
- D. Jeske, M. Xie (eds.), Applied Stochastic Models in Business and Industry Special Issue on Data Fusion, vol. 34, no. 1 (Wiley, New York, 2018)
- K. Lee, K. Carlberg, Model Reduction of Dynamical Systems on Nonlinear Manifolds Using Deep Convolutional Autoencoders (2018), arXiv preprint arXiv:1812.08373 · arXiv
- S.M. Moosavi et al., Capturing chemical intuition in synthesis of metalorganic frameworks. Nat. Commun. 10, 539 (2019)
- by C. Catlett, W. Gentzsch, L. Grandinetti (IOS Press, Amster- dam, 2013), pp. 212-230
- A. Paszke, S. Gross, C. Soumith, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, Automatic differen- tiation in PyTorch, in Proceedings of NIPS-W (2017)
- D. Rolnick et al., Tackling Climate Change with Machine Learning, https://arxiv.org/pdf/1906.05433.pdf (2019) · arXiv
- C. Szegedy et al., Intriguing properties of neural networks, in International Conference on Learning Representations (2014)
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Humphreys, D., Kupresanin, A., Boyer, M. D., Canik, J., Chang, C. S., Cyr, E. C., Granetz, R., Hittinger, J., Kolemen, E., Lawrence, E., Pascucci, V., Patra, A., & Schissel, D.
- Affiliations
- General Atomics (United States) (US)
- Lawrence Livermore National Laboratory (US)
- Princeton Plasma Physics Laboratory (US)
- Oak Ridge National Laboratory (US)
- Sandia National Laboratories (US)
- Massachusetts Institute of Technology (US)
- Princeton University (US)
- Los Alamos National Laboratory (US)
- University of Utah (US)
- University at Buffalo, State University of New York (US)
- Corresponding author
- Humphreys, David
- ORCID
- Venue
- Journal of Fusion Energy
- Published
- 2020-08
- Volume
- 39
- Issue
- 4
- Pages
- 123-155
- ISSN
- 0164-0313 (ISSN-L)
- 1572-9591
- OpenAlex
- W3088635668
- Cited by
- 47
- References
- 67
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 8 · 2023: 7 · 2022: 8 · 2021: 2 · 2020: 1
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Fusion Energy (publishedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy
- Topics
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Magnetic confinement fusion research
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Fusion and Plasma Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Fusion materials and technologies
- Keywords
- machine learning, artificial intelligence, fusion research, research needs, disruption prediction, surrogate models, experimental planning, uncertainty quantification, model validity, ITER
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
