The DOI matches its Crossref record; 62 of the 69 references it cites were found in a registry or an index.
Probabilistic climate change projections using neural networks
Open accessAbstract
Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.
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.
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?
In the citing paper
“Calculations from this model revealed that almost half the members surpassed the surface heating range that the IPCC estimated earlier.”
From Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action (2022) · cites this paper as Knutti et al., 2003
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
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Climate change 2001 : the scientific basis
Houghton, J., Ding, Y., Griggs, D., Noguer, M., van der Linden, P. V. D., Dai, X., Maskell, K., & Johnson, C. (2001) · Foreign Affairs · doi:10.2307/20033020 · Cited by 14,162
Training feedforward networks with the Marquardt algorithm
Hagan, M., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,778
Special report on emissions scenarios
Nakićenović, N., Alcamo, J., Davis, G. R., de Vries, B. J. M., Fenhann, J. V., Gaffin, S., Gregory, K., Grübler, A., Jung, T. Y., Kram, T., et al. (2000) · Cited by 6,089
Hagan, M., Demuth, H. B., Beale, M. H., & Jes?s, O. D. (2016) · Institutional Repository of the National University Open and Distance UNAD (Universidad Nacional Abierta y a Distancia) · Cited by 5,133
Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model
Cox, P. M., Betts, R., JONES, C. D., Spall, S. A., & Totterdell, I. J. (2000) · Nature · doi:10.1038/35041539 · Cited by 4,169
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Crämer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., et al. (2003) · Global Change Biology · doi:10.1046/j.1365-2486.2003.00569.x · Cited by 3,343
Perceptrons: An Introduction to Computational Geometry
Minsky, M., & Papert, S. A. (1969) · Cited by 3,273
Mullin, A., & Rosenblatt, F. F. (1963) · American Mathematical Monthly · doi:10.2307/2312103 · Cited by 2,211
Causes of Climate Change Over the Past 1000 Years
Crowley, T. J. (2000) · Science · doi:10.1126/science.289.5477.270 · Cited by 2,068
Flagged first, then by citations
7 of the 69 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Church JA, Gregory JM (2001) Changes in sea level. Climate change 2001: the scientific basis. In: Houghton JT et al. (eds) Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, pp 639-693
- Legget J, Pepper WJ, Swart RJ (1992) Climate change 1992. The Supplementary Report to the IPCC Scientific Assessment, Ch Emissions Scenarios for IPCC: an Update. Cambridge Uni- versity Press, Cambridge, UK, pp 69-95
- Shine KP, Derwent RG, Wuebbles DJ, Morcrette JJ (1995) Radi- ative forcing of climate. In: Houghton JY, Jenkins GJ, Ephraums JJ (eds) Climate change, the IPCC scientific assess- ment, Cambridge University Press, Cambridge, UK
Cited by
Sorted by citations
Items 101–194 of 194
Tomassini, L., Knutti, R., Plattner, G., van Vuuren, D. P., Stocker, T. F., Howarth, R. B., & Borsuk, M. E. (2010) · Climatic Change · doi:10.1007/s10584-009-9763-3 · Cited by 21
Self-citationEnvironmental change: key issues and alternative perspectives
(2006) · Choice Reviews Online · doi:10.5860/choice.43-4722 · Cited by 20
Li, S., Rupp, D. E., Hawkins, L., Mote, P. W., McNeall, D., Sparrow, S., Wallom, D. C. H., Betts, R., & Wettstein, J. J. (2019) · Geoscientific model development · doi:10.5194/gmd-12-3017-2019 · Cited by 19
The coupling of optimal economic growth and climate dynamics
Bahn, O., Drouet, L., Edwards, N. R., Haurie, A. B., Knutti, R., Kypreos, S., Stocker, T. F., & Vial, J.-P. (2006) · Climatic Change · doi:10.1007/s10584-006-9108-4 · Cited by 19
Self-citationCalculating the turbulent fluxes in the atmospheric surface layer with neural networks
Leufen, L. H., & Schädler, G. (2019) · Geoscientific model development · doi:10.5194/gmd-12-2033-2019 · Cited by 18
Energy policies avoiding a tipping point in the climate system
Bahn, O., Edwards, N. R., Knutti, R., & Stocker, T. F. (2010) · Energy Policy · doi:10.1016/j.enpol.2010.10.002 · Cited by 18
Self-citationModeling Ecological Success of Common Pool Resource Systems Using Large Datasets
Frey, U. J., & Rusch, H. (2014) · World Development · doi:10.1016/j.worlddev.2014.01.034 · Cited by 18
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_8 · Cited by 18
Self-citationSokolov, A., & Monier, E. (2012) · Journal of Climate · doi:10.1175/jcli-d-11-00590.1 · Cited by 16
The Emergence of Stimulus Relations: Human and Computer Learning
Ninness, C., Ninness, S. K., Rumph, M., & Lawson, D. M. (2017) · Perspectives on Behavior Science · doi:10.1007/s40614-017-0125-6 · Cited by 16
Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties
Libardoni, A. G., Forest, C. E., Sokolov, A., & Monier, E. (2019) · Geophysical Research Letters · doi:10.1029/2019gl082442 · Cited by 16
Schleussner, C., Frieler, K., Meinshausen, M. A., Yin, J., & Levermann, A. (2011) · Earth System Dynamics · doi:10.5194/esd-2-191-2011 · Cited by 16
Strobach, E., & Bel, G. (2020) · Nature Communications · doi:10.1038/s41467-020-14342-9 · Cited by 15
de Amorim, P. B., Barfus, K., Weiss, H., & Bernhofer, C. (2014) · Environmental Earth Sciences · doi:10.1007/s12665-014-3301-y · Cited by 14
Stabilizing global temperature change below thresholds: Monte Carlo analyses with MERGE
Kypreos, S. (2007) · Computational Management Science · doi:10.1007/s10287-007-0049-9 · Cited by 14
Sokolov, A., Forest, C. E., & Stone, P. H. (2009) · Climate Dynamics · doi:10.1007/s00382-009-0556-1 · Cited by 14
Bayesian estimation of climate sensitivity using observationally constrained simple climate models
Bodman, R. W., & Jones, R. N. (2016) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.397 · Cited by 14
Ensembling geophysical models with Bayesian Neural Networks
Sengupta, U., Amos, M., Hosking, J. S., Rasmussen, C. E., Juniper, M. P., & Young, P. J. (2020) · NERC Open Research Archive (Natural Environment Research Council) · doi:10.17863/cam.60032 · Cited by 13
Uncertainty in humanities network visualization
Conroy, M., Gillmann, C., Harvey, F., Mchedlidze, T., Fabrikant, S. I., Windhager, F., Scheuermann, G., Tangherlini, T. R., Warren, C., Weingart, S., et al. (2024) · Frontiers in Communication · doi:10.3389/fcomm.2023.1305137 · Cited by 13
Alinezhad, A., Gohari, A., Eslamian, S., & Saberi, Z. (2021) · Theoretical and Applied Climatology · doi:10.1007/s00704-021-03575-3 · Cited by 13
Neural Network Modeling in Climate Change Studies
Pasini, A. (2008) · Springer eBooks · doi:10.1007/978-1-4020-9119-3_12 · Cited by 12
Observed climate change constrains the likelihood of extreme future global warming
Stott, P. A., Huntingford, C., JONES, C. D., & Kettleborough, J. A. (2008) · Tellus B · doi:10.1111/j.1600-0889.2007.00329.x · Cited by 12
Egeonu, D., Njoku, H. O., Okolo, P. N., & Enibe, S. O. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-13572-4_9 · Cited by 10
A data-based model to locate mass movements triggered by seismic events in Sichuan, China
de Souza, F. T. (2013) · Environmental Monitoring and Assessment · doi:10.1007/s10661-013-3400-3 · Cited by 10
Diodato, N., & Bellocchi, G. (2013) · Theoretical and Applied Climatology · doi:10.1007/s00704-013-0915-z · Cited by 10
Salami, E. S., & Ehteshami, M. (2016) · Modeling Earth Systems and Environment · doi:10.1007/s40808-016-0094-x · Cited by 10
Model Hierarchy and Simplified Climate Models
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_2 · Cited by 10
Self-citationAssessing variance components of general circulation model output fields
Furrer, R., Geinitz, S., & Sain, S. R. (2012) · Environmetrics · doi:10.1002/env.2139 · Cited by 8
Comments on “Why Hasn’t Earth Warmed as Much as Expected?”
Knutti, R., & Plattner, G. (2011) · Journal of Climate · doi:10.1175/2011jcli4038.1 · Cited by 8
Self-citationEstimates of climate system properties incorporating recent climate change
Libardoni, A. G., Forest, C. E., Sokolov, A., & Monier, E. (2018) · Advances in statistical climatology, meteorology and oceanography · doi:10.5194/ascmo-4-19-2018 · Cited by 8
Libardoni, A. G., Forest, C. E., Sokolov, A., & Monier, E. (2018) · Geoscientific model development · doi:10.5194/gmd-11-3313-2018 · Cited by 7
Hot questions of temperature bias
Forest, C. E., & Reynolds, R. W. (2008) · Nature · doi:10.1038/453601a · Cited by 7
Hydrometeorological hazards under future climate change
Edwards, T. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139047562.007 · Cited by 6
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.016 · Cited by 6
Modelling Climate Change Impacts for Adaptation Assessments
Dessai, S., & van der Sluijs, J. P. (2011) · Wiley eBooks · doi:10.1002/9781119951445.ch6 · Cited by 6
Lohou, F., Lothon, M., Bastin, S., Brut, A., Canut, G., Cohard, J., Cheruy, F., Couvreux, F., Dupont, S., Lafont, S., et al. (2025) · Journal of the European Meteorological Society. · doi:10.1016/j.jemets.2025.100019 · Cited by 5
Evaluating the Use of Ocean Models of Different Complexity in Climate Change Studies
Sokolov, A., Dutkiewicz, S., Stone, P. H., & Scott, J. R. (2005) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5
Zouzoua, M., Bastin, S., Chiriaco, M., Lohou, F., Lothon, M., Jomé, M., Mallet, C., Barthès, L., & Canut, G. (2025) · Geoscientific model development · doi:10.5194/gmd-18-3211-2025 · Cited by 4
Mind the Rate! Why the Rate of Global Climate Change Matters, and How Much
Ambrosi, P. (2008) · The MIT Press eBooks · doi:10.7551/mitpress/7760.003.0021 · Cited by 4
Predicting Global Average Temperature Time Series Using an Entire Graph Node Training Approach
Wang, Z., Chen, Z., Shi, Z., & Gao, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3480888 · Cited by 3
Iranmanesh, S. H., Keshavarz, M., & Abdollahzade, M. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-58088-3_15 · Cited by 3
Masanganise, J., Mapuwei, T. W., Magodora, M., & Basira, K. (2014) · Cited by 3
A Data Mining approach to predict mass movements induced by seismic events in Sichuan, China
de Souza, F. T., & Wang, Z. (2010) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2010.5583659 · Cited by 2
Artificial Neural Network to Serve Scenario Analysis Purposes
Hassani, B. K. (2016) · Springer eBooks · doi:10.1007/978-3-319-25056-4_9 · Cited by 2
Construction of High Resolution Thermocline Grid Data Sets
Hu, C., Zhang, T., Wang, J., Gou, Y., Wang, K., Bai, H., & Jiang, Y. L. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-7605-3_58 · Cited by 2
Fels, A. E. A. E., Alaa, N. E., Bachnou, A., & Barrimi, O. E. (2022) · Earth Science Informatics · doi:10.1007/s12145-022-00784-w · Cited by 2
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_1 · Cited by 2
Self-citationAn introduction to models and modelling
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.003 · Cited by 2
Mosadegh, E., & Babaeian, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04619 · Cited by 2
A Bibliometric Mapping of Artificial Intelligence Applications in Climate Change Research
Abdullah, K. H., & Baharom, Z. (2025) · Fronteiras Journal of Social Technological and Environmental Science · doi:10.21664/2238-8869.2025v14i3.7927 · Cited by 1
The coupling of optimal economic growth and climate dynamics
Bahn, O., Drouet, L., Edwards, N. R., Haurie, A. B., Knutti, R., Kypreos, S., Stocker, T. F., & Vial, J.-P. (2007) · Springer eBooks · doi:10.1007/978-1-4020-5714-4_6 · Cited by 1
Self-citationDelgado-Enales, I., Molina-Costa, P., & Ser, J. D. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422134 · Cited by 1
Communicating the Probabilities of Extreme Surface Temperature Outcomes
Rive, N. A., & Myhre, G. (2012) · Atmospheric and Climate Sciences · doi:10.4236/acs.2012.24049 · Cited by 1
Chapter 3 Climate and Invasive Species: The Limits to Climate Information
New, M. G., & McSweeney, C. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199560158.003.0003 · Cited by 1
Τολίκα, Κ., Maheras, P., Vafiadis, M., & Αnagnostopoulou, C. (2007) · Climatologie · doi:10.4267/climatologie.726 · Cited by 1
Initial Value and Boundary Value Problems
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_5 · Cited by 1
Self-citationChallenges posed by and approaches to the study of seasonal-to-decadal climate variability
Schwierz, C., Davies, H. C., Appenzeller, C., Liniger, M. A., Müller, W. A., Stocker, T. F., & Yoshimori, M. (2007) · Springer eBooks · doi:10.1007/978-1-4020-5714-4_3 · Cited by 1
Self-citationThe Anthropocene: changing aquatic environments and ecosystems
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.011
Large-Scale Circulation in the Atmosphere
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_7
Self-citationFuture global mean temperatures and sea-level
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.014
The transition from the last glacial maximum to the Holocene
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.007
Multiple Equilibria in the Climate System
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_9
Self-citationEnergy Transport in the Climate System and Its Parameterisation
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_4
Self-citationGlacial and interglacial worlds
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.006
Attention au rythme du changement climatique !
Ambrosi, P. (2006) · Natures Sciences Sociétés · doi:10.1051/nss:2006018
KyotoPlus - Papers <2°C Trajectories-a Brief Background Note
Meinshausen, M. A. (2006)
(2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.018
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.013
The role and potential of Quaternary research in constraining future climate sensitivity
Yoshimori, M., & Abe‐Ouchi, A. (2009) · The Quaternary Research (Daiyonki-Kenkyu) · doi:10.4116/jaqua.48.143
Assessment of Optimum Neural Network Architecture in Forecasting and Mining Carbon Emissions
Poornashankar, & Pawar, V. P. (2011) · International Journal of Computer and Electrical Engineering · doi:10.7763/ijcee.2011.v3.317
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.012
The palaeo-record: approaches, timeframes and chronology
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.004
Kheshgi, H. S. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511619472.002
The Palaeo-record: archives, proxies and calibration
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.005
Defining and exploring the key questions
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.002
The Anthropocene – changing land
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.010
Sceptics, responses and partial answers
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.017
The Anthropocene – a changing atmosphere
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.009
From the global to the specific
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.015
Bharadwaj, S. S., & Koundinya, A. K. (2016) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss.2016.7779370
On energy modelling for a range of spatial, temporal and technological scales
Keppo, I. (2009) · Aaltodoc (Aalto University)
Modeling Success of Social-Ecological Systems with Artificial Neural Networks
Frey, U. J., & Rusch, H. (2014)
A Review of Ocean Dynamics in the North Atlantic: Achievements and Challenges
Seip, K. L. (2020) · Climate · doi:10.3390/cli8040049
Padmavathi, B., Tiwari, R. K., & Tiwari, V. M. (2021) · Pure and Applied Geophysics · doi:10.1007/s00024-021-02724-z
Moreno, R. S. (2018) · Springer theses · doi:10.1007/978-3-319-99450-5_2
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.001
Oldfield, F. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9781139165266.008
Frey, U. J. (2017) · Springer eBooks · doi:10.1007/978-3-662-55446-3_4
Drivers and Impacts of Text Mining on Climate Change
Selvakumar, P., Seenivasan, S., Athavale, V. A., Poorani, S., & Das, A. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-7230-2.ch018
Large-Scale Circulation in the Ocean
Stocker, T. F. (2011) · Advances in geophysical and environmental mechanics and mathematics · doi:10.1007/978-3-642-00773-6_6
Self-citationMassardier-Galatà, L. (2017) · theses.fr (ABES) · doi:10.70675/38aa643ezce02z4cbez8136z7237597408b5
Metadata
- Authors
- Knutti, R., Stocker, T. F., Joos, F., & Plattner, G.-K.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Knutti, R.
- Venue
- Climate Dynamics
- Published
- 2003-09-01
- Volume
- 21
- Issue
- 3-4
- Pages
- 257-272
- ISSN
- 0930-7575 (ISSN-L)
- 1432-0894
- OpenAlex
- W2167220187
- Cited by
- 197
- References
- 69
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 6 · 2017: 7 · 2016: 8 · 2015: 6 · 2014: 8 · 2013: 9 · 2012: 8
- FWCI
- 15.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Climate Dynamics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Keywords
- climate change projections, neural network surrogate model, climate model ensembles, climate sensitivity, radiative forcing, carbon cycle uncertainty, ocean heat uptake, surface warming, probability density function, emission scenarios, IPCC projections, model parameter uncertainty
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
