A formal theory of inductive inference. Part II
Open accessThe 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?
The ability to generalise is closely related to compression, with more compressed representations yielding better accuracy.
“The ability to generalise is closely related to compression [14,15], with more compressed representations yielding better accuracy [13] for the same reason that there is only one straight line interpolating any two points, but infinitely many polynomials of higher degree.”
From Compression, The Fermi Paradox and Artificial Super-Intelligence (2022) · cites this paper as [15]
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
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283 · see citing works
Banks, D. L., Ríos, J., & Insua, D. R. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18653-3 · Cited by 3,649 · see citing works
Three models for the description of language
Chomsky, N. (1956) · IRE Transactions on Information Theory · doi:10.1109/tit.1956.1056813 · Cited by 2,846 · see citing works
Keynes, J. M. (1973) · Palgrave Macmillan UK eBooks · doi:10.1007/978-1-349-00843-8 · Cited by 2,576 · see citing works
Logical Foundations of Probability
Savage, L. J., & Carnap, R. (1952) · Econometrica · doi:10.2307/1907654 · Cited by 2,042 · see citing works
Computer Languages for Symbol Manipulation
Green, B. F. (1961) · IRE Transactions on Electronic Computers · doi:10.1109/tec.1961.5219281 · Cited by 11 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [4]4. SOLOMONOFF, R. J. (1959), "A Progress Report on Machines to Learn to Translate Languages and Retrieve Information," pp. 941-953. Advances in Documenta- tion and Library Sciences, Vol III, Part 2. Interscience, New York. AFOSR TN-59-646 (Contract AF 49(638)-376); ZTB-134.
- [5]5. SOLOMONOFF, R. J. (1960), The mechanization of linguistic learning. Proc. Second Intern. Congr. Cybernetics, Namur, Belgium, September, 1958, pp. 180-193. AFOSR TN-59-246 (Contract AF 49(638)-376); ASTIA AD No. 212 226; ZTB- 125.
- [6]6. VAN HEERDEN, P. J'. (1963), "A General Theory of Prediction." Polaroid Corp. Cambridge 39, Mass. (Privately circulated report).
Cited by
Sorted by citations
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
Prediction, learning, and games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 4,660 · see citing works
How to construct random functions
Goldreich, O., Goldwasser, S., & Micali, S. M. (1986) · Journal of the ACM · doi:10.1145/6490.6503 · Cited by 2,108 · see citing works
Pappu, R., Recht, B., Taylor, J., & Gershenfeld, N. (2002) · Science · doi:10.1126/science.1074376 · Cited by 2,033 · see citing works
A Universal Prior for Integers and Estimation by Minimum Description Length
Rissanen, J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346150 · Cited by 1,655 · see citing works
Inductive Logic Programming: Theory and methods
Muggleton, S., & de Raedt, L. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90035-3 · Cited by 1,537 · see citing works
Concrete Problems in AI Safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314 · see citing works
The definition of random sequences
Martin-Löf, P. (1966) · Information and Control · doi:10.1016/s0019-9958(66)80018-9 · Cited by 1,208 · see citing works
A theory and methodology of inductive learning
Michalski, R. S. (1983) · Artificial Intelligence · doi:10.1016/0004-3702(83)90016-4 · Cited by 1,205 · see citing works
Bayesian Artificial Intelligence
Korb, K. B., & Nicholson, A. E. (2003) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203491294 · Cited by 1,074 · see citing works
Muggleton, S. (1991) · New Generation Computing · doi:10.1007/bf03037089 · Cited by 1,044 · see citing works
The minimum description length principle in coding and modeling
Barron, A. R., Rissanen, J., & Yu, B. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720554 · Cited by 1,004 · see citing works
Algorithmic Information Theory
Chaitin, G. J. (1977) · IBM Journal of Research and Development · doi:10.1147/rd.214.0350 · Cited by 982 · see citing works
Information Science and Statistics
Andersen, E. B. (1986) · COMPSTAT · doi:10.1007/978-3-642-46890-2_1 · Cited by 910 · see citing works
Probabilistic models of information retrieval based on measuring the divergence from randomness
Amati, G., & van Rijsbergen, C. J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582416 · Cited by 886 · see citing works
Inductive Inference: Theory and Methods
Angluin, D., & Smith, C. H. (1983) · ACM Computing Surveys · doi:10.1145/356914.356918 · Cited by 872 · see citing works
A Theory of Program Size Formally Identical to Information Theory
Chaitin, G. J. (1975) · Journal of the ACM · doi:10.1145/321892.321894 · Cited by 831 · see citing works
The calculi of emergence: computation, dynamics and induction
Crutchfield, J. P. (1994) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(94)90273-9 · Cited by 748 · see citing works
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747 · see citing works
Inductive inference of formal languages from positive data
Angluin, D. (1980) · Information and Control · doi:10.1016/s0019-9958(80)90285-5 · Cited by 743 · see citing works
On Defining Artificial Intelligence
Wang, P. (2019) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2019-0002 · Cited by 740 · see citing works
Machine Learning: The art and science of algorithms that make sense of data
Flach, P. A. (2012) · Cited by 694 · see citing works
Toward a mathematical theory of inductive inference
Blum, L., & Blum, M. (1975) · Information and Control · doi:10.1016/s0019-9958(75)90261-2 · Cited by 662 · see citing works
Zvonkin, A. K., & Levin, L. A. (1970) · Russian Mathematical Surveys · doi:10.1070/rm1970v025n06abeh001269 · Cited by 650 · see citing works
Consistent interpretations of quantum mechanics
Omnès, R. (1992) · Reviews of Modern Physics · doi:10.1103/revmodphys.64.339 · Cited by 648 · see citing works
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 636 · see citing works
Theory of Decision under Uncertainty
Gilboa, I. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511840203 · Cited by 598 · see citing works
A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations.
Wagemans, J. P., Feldman, J., Gepshtein, S., Kimchi, R., Pomerantz, J. R., van der Helm, P. A., & van Leeuwen, C. (2012) · Psychological Bulletin · doi:10.1037/a0029334 · Cited by 555 · see citing works
Formal models of language learning
Pinker, S. A. (1979) · Cognition · doi:10.1016/0010-0277(79)90001-5 · Cited by 547 · see citing works
de la Higuera, C. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9781139194655 · Cited by 535 · see citing works
Estimation and Inference by Compact Coding
Wallace, C. S., & Freeman, P. R. (1987) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1987.tb01695.x · Cited by 521 · see citing works
Complexity as thermodynamic depth
Lloyd, S., & Pagels, H. R. (1988) · Annals of Physics · doi:10.1016/0003-4916(88)90094-2 · Cited by 504 · see citing works
Easily calculable measure for the complexity of spatiotemporal patterns
Kaspar, F., & Schuster, H. G. (1987) · Physical Review A · doi:10.1103/physreva.36.842 · Cited by 504 · see citing works
Predictability, Complexity, and Learning
Bialek, W., Nemenman, I. M., & Tishby, N. (2001) · Neural Computation · doi:10.1162/089976601753195969 · Cited by 504 · see citing works
Minimum complexity density estimation
Barron, A. R., & Cover, T. M. (1991) · IEEE Transactions on Information Theory · doi:10.1109/18.86996 · Cited by 497 · see citing works
Random sequence generation by cellular automata
Wolfram, S. (1986) · Advances in Applied Mathematics · doi:10.1016/0196-8858(86)90028-x · Cited by 494 · see citing works
How Efficiency Shapes Human Language
Gibson, E., Futrell, R., Piantadosi, S. P., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.003 · Cited by 485 · see citing works
Minimization of Boolean complexity in human concept learning
Feldman, J. (2000) · Nature · doi:10.1038/35036586 · Cited by 475 · see citing works
Poverty of the Stimulus Revisited
Berwick, R. C., Pietroski, P. M., Yankama, B., & Chomsky, N. (2011) · Cognitive Science · doi:10.1111/j.1551-6709.2011.01189.x · Cited by 447 · see citing works
Crutchfield, J. P. (2011) · Nature Physics · doi:10.1038/nphys2190 · Cited by 435 · see citing works
Applying Occam’s razor in modeling cognition: A Bayesian approach
Myung, I. J., & Pitt, M. A. (1997) · Psychonomic Bulletin & Review · doi:10.3758/bf03210778 · Cited by 409 · see citing works
A Material Theory of Induction
Norton, J. D. (2003) · Philosophy of Science · doi:10.1086/378858 · Cited by 409 · see citing works
Generalized statistical complexity measures: Geometrical and analytical properties
Martín, M. T., Plastino, A. R., & Rosso, O. A. (2006) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.11.053 · Cited by 387 · see citing works
Predictability: a way to characterize complexity
Boffetta, G. (2002) · Physics Reports · doi:10.1016/s0370-1573(01)00025-4 · Cited by 387 · see citing works
Complexity-based induction systems: Comparisons and convergence theorems
Solomonoff, R. J. (1978) · IEEE Transactions on Information Theory · doi:10.1109/tit.1978.1055913 · Cited by 379 · see citing works
Self-citationIntegrated information theory of consciousness: an updated account.
(2012) · PubMed · doi:10.4449/aib.v149i5.1388 · Cited by 370 · see citing works
Statistical and Inductive Inference by Minimum Message Length
Wallace, C. S. (2005) · Springer eBooks · doi:10.1007/0-387-27656-4 · Cited by 369 · see citing works
Benedetto, D., Caglioti, E., & Loreto, V. (2002) · Physical Review Letters · doi:10.1103/physrevlett.88.048702 · Cited by 359 · see citing works
Computational Mechanics: Pattern and Prediction, Structure and Simplicity
Shalizi, C. R., & Crutchfield, J. P. (2001) · Journal of Statistical Physics · doi:10.1023/a:1010388907793 · Cited by 471 · see citing works
Minimum Message Length and Kolmogorov Complexity
Wallace, C. S. (1999) · The Computer Journal · doi:10.1093/comjnl/42.4.270 · Cited by 343 · see citing works
The Search for Simplicity: A Fundamental Cognitive Principle?
Chater, N. (1999) · The Quarterly Journal of Experimental Psychology Section A · doi:10.1080/713755819 · Cited by 327 · see citing works
Verdú, S. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720531 · Cited by 320 · see citing works
Combinatorial foundations of information theory and the calculus of probabilities
Kolmogorov, A. N. (1983) · Russian Mathematical Surveys · doi:10.1070/rm1983v038n04abeh004203 · Cited by 307 · see citing works
A tutorial introduction to Bayesian models of cognitive development
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011) · Cognition · doi:10.1016/j.cognition.2010.11.015 · Cited by 300 · see citing works
The learnability of abstract syntactic principles
Perfors, A., Tenenbaum, J. B., & Regier, T. (2010) · Cognition · doi:10.1016/j.cognition.2010.11.001 · Cited by 288 · see citing works
Making sense of randomness: Implicit encoding as a basis for judgment.
Falk, R., & Konold, C. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.301 · Cited by 282 · see citing works
Off-line dictionary-based compression
Larsson, N. J., & Moffat, A. (2000) · Proceedings of the IEEE · doi:10.1109/5.892708 · Cited by 279 · see citing works
Thermodynamic cost of computation, algorithmic complexity and the information metric
Zurek, W. H. (1989) · Nature · doi:10.1038/341119a0 · Cited by 272 · see citing works
Modern Cryptography, Probabilistic Proofs and Pseudorandomness
Goldreich, O. (1999) · Algorithms and combinatorics · doi:10.1007/978-3-662-12521-2 · Cited by 270 · see citing works
Grammatical Inference: Introduction and Survey - Part I
Fu, K., & Booth, T. L. (1975) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1975.5409159 · Cited by 268 · see citing works
Approximation of nonlinear systems with radial basis function neural networks
Schilling, R. J., Carroll, J., & Al‐Ajlouni, A. F. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.896792 · Cited by 263 · see citing works
Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts
Schmidhuber, J. (2006) · Connection Science · doi:10.1080/09540090600768658 · Cited by 261 · see citing works
A Survey of Russian Approaches to Perebor (Brute-Force Searches) Algorithms
Trakhtenbrot, B. A. (1984) · IEEE Annals of the History of Computing · doi:10.1109/mahc.1984.10036 · Cited by 260 · see citing works
Information-geometric approach to inferring causal directions
Janzing, D., Mooij, J. M., Zhang, K., Lemeire, J., Jakob Zscheischler, A., Daniušis, P., Steudel, B., & Schölkopf, B. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.01.002 · Cited by 259 · see citing works
Competitive On-Line Statistics
Vovk, V. (2001) · International Statistical Review · doi:10.1111/j.1751-5823.2001.tb00457.x · Cited by 256 · see citing works
Process complexity and effective random tests
Schnorr, C. P. (1973) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(73)80030-3 · Cited by 254 · see citing works
Almost everywhere high nonuniform complexity
Lutz, J. H. (1992) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(92)90020-j · Cited by 251 · see citing works
Is “the Theory of Everything” Merely the Ultimate Ensemble Theory?
Tegmark, M. (1998) · Annals of Physics · doi:10.1006/aphy.1998.5855 · Cited by 250 · see citing works
Probabilistic Incremental Program Evolution
Salustowicz, R., & Schmidhuber, J. (1997) · Evolutionary Computation · doi:10.1162/evco.1997.5.2.123 · Cited by 249 · see citing works
Werbos, P. J. (1987) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1987.289329 · Cited by 249 · see citing works
Randomness conservation inequalities; information and independence in mathematical theories
Levin, L. A. (1984) · Information and Control · doi:10.1016/s0019-9958(84)80060-1 · Cited by 246 · see citing works
A unified approach to the definition of random sequences
Schnorr, C. P. (1971) · Theory of Computing Systems · doi:10.1007/bf01694181 · Cited by 244 · see citing works
Minimum description length induction, Bayesianism, and Kolmogorov complexity
Vitanyi, P. M. B., & Li, M. (2000) · IEEE Transactions on Information Theory · doi:10.1109/18.825807 · Cited by 241 · see citing works
A bibliographical study of grammatical inference
de la Higuera, C. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.01.003 · Cited by 235 · see citing works
Causal Inference Using the Algorithmic Markov Condition
Janzing, D., & Schölkopf, B. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2060095 · Cited by 230 · see citing works
Reconciling simplicity and likelihood principles in perceptual organization.
Chater, N. (1996) · Psychological Review · doi:10.1037/0033-295x.103.3.566 · Cited by 226 · see citing works
Inductive inference, DFAs, and computational complexity
Pitt, L. (1989) · Lecture notes in computer science · doi:10.1007/3-540-51734-0_50 · Cited by 222 · see citing works
Causal architecture, complexity and self-organization in time series and cellular automata
Shalizi, C. R., & Olsson, M. (2001) · Cited by 221 · see citing works
Methods and Techniques of Complex Systems Science: An Overview
Shalizi, C. R. (2007) · Springer eBooks · doi:10.1007/978-0-387-33532-2_2 · Cited by 210 · see citing works
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement
Hernández‐Orallo, J. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9505-7 · Cited by 208 · see citing works
Measuring universal intelligence: Towards an anytime intelligence test
Hernández‐Orallo, J., & Dowe, D. L. (2010) · Artificial Intelligence · doi:10.1016/j.artint.2010.09.006 · Cited by 199 · see citing works
Minimal representation multisensor fusion using differential evolution
Joshi, R., & Sanderson, A. C. (1999) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.736361 · Cited by 196 · see citing works
A philosophical basis for hydrological uncertainty
Nearing, G., Tian, Y., Gupta, H. V., Clark, M., Harrison, K. W., & Weijs, S. V. (2016) · Hydrological Sciences Journal · doi:10.1080/02626667.2016.1183009 · Cited by 194 · see citing works
Unsupervised Language AcquisitionPreprint
de Marcken, C. (1996) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9611002 · Cited by 194 · see citing works
Kolmogorov Complexity and its Applications
Li, M., & Vitanyi, P. M. B. (1990) · Elsevier eBooks · doi:10.1016/b978-0-444-88071-0.50009-6 · Cited by 188 · see citing works
The dimensions of individual strings and sequences
Lutz, J. H. (2003) · Information and Computation · doi:10.1016/s0890-5401(03)00187-1 · Cited by 184 · see citing works
Generalizing Case Frames Using a Thesaurus and the MDL PrinciplePreprint
Li, H., & Abe, N. (1995) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9507011 · Cited by 182 · see citing works
Some decidability results on grammatical inference and complexity
Feldman, J. A. (1972) · Information and Control · doi:10.1016/s0019-9958(72)90424-x · Cited by 175 · see citing works
Violation of a Bell inequality in two-dimensional orbital angular momentum state-spaces
Leach, J., Jack, B., Romero, J., Ritsch‐Marte, M., Boyd, R. W., Jha, A. K., Barnett, S. M., Franke‐Arnold, S., & Padgett, M. J. (2009) · Optics Express · doi:10.1364/oe.17.008287 · Cited by 173 · see citing works
Schmidhuber, J., Zhao, J., & Wiering, M. (1997) · Machine Learning · doi:10.1023/a:1007383707642 · Cited by 167 · see citing works
How To Construct Random Functions
Goldreich, O., Goldwasser, S., & Micali, S. M. (1984) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1984.715949 · Cited by 167 · see citing works
MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions
Wallace, C. S., & Dowe, D. L. (2000) · Statistics and Computing · doi:10.1023/a:1008992619036 · Cited by 167 · see citing works
A Monte-Carlo AIXI Approximation
Veness, J., Ng, K. S., Hütter, M., Uther, W., & Silver, D. (2011) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3125 · Cited by 165 · see citing works
Thinking Outside the Box: Rectilinear Shapes Selectively Activate Scene-Selective Cortex
Nasr, S., Echavarria, C. E., & Tootell, R. B. H. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4802-13.2014 · Cited by 163 · see citing works
Counting probability distributions: Differential geometry and model selection
Myung, I. J., Balasubramanian, V., & Pitt, M. A. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.170283897 · Cited by 163 · see citing works
Optimal Ordered Problem Solver
Schmidhuber, J. (2004) · Machine Learning · doi:10.1023/b:mach.0000015880.99707.b2 · Cited by 162 · see citing works
Model Selection Based on Minimum Description Length
Grünwald, P. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1999.1280 · Cited by 162 · see citing works
Weight Agnostic Neural Networks
Gaier, A., & Ha, D. (2019) · Unpublished · doi:10.13140/rg.2.2.16025.88169 · Cited by 161 · see citing works
Reinforcement Learning Neural Turing Machines - RevisedPreprint
Zaremba, W., & Sutskever, I. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00521 · Cited by 161 · see citing works
- Authors
- Solomonoff, R. J.
- Corresponding author
- Solomonoff, R.J.
- Venue
- Information and Control
- Publisher
- Elsevier BV
- Published
- 1964-06
- Volume
- 7
- Issue
- 2
- Pages
- 224-254
- ISSN
- 0019-9958 (ISSN-L)
- OpenAlex
- W2135625884
- Cited by
- 1,758
- References
- 6
- Citations by year
- 2026: 43 · 2025: 16 · 2024: 18 · 2023: 22 · 2022: 35 · 2021: 52 · 2020: 60 · 2019: 40 · 2018: 60 · 2017: 67 · 2016: 64 · 2015: 75 · 2014: 59 · 2013: 94 · 2012: 88
- FWCI
- 1.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information and Control (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- inductive reasoning, universal Turing machine, phrase structure grammar, prediction accuracy
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
