The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Support vector machines for predicting distribution of Sudden Oak Death in California
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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
John Lu, Z. Q. (2010) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,390
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS
De'ath, G., & Fabricius, K. E. (2000) · Ecology · doi:10.1890/0012-9658(2000)081[3178:cartap]2.0.co;2 · Cited by 3,167
Guisan, A., Edwards, T. C., & Hastie, T. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00204-1 · Cited by 2,356
Cited by
Niches and distributional areas: Concepts, methods, and assumptions
Soberón, J., & Nakamura, M. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901637106 · Cited by 1,306
Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2022) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 830
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., & Brenning, A. (2019) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.06.002 · Cited by 614
Yao, X., Tham, L. G., & Dai, F. C. (2008) · Geomorphology · doi:10.1016/j.geomorph.2008.02.011 · Cited by 600
openModeller: a generic approach to species’ potential distribution modelling
de Souza Muñoz, M. E., De Giovanni, R., de Siqueira, M. F., Sutton, T., Brewer, P., Pereira, R. S., Canhos, D. A. L., & Canhos, V. P. (2011) · GeoInformatica · doi:10.1007/s10707-009-0090-7 · Cited by 286
Metadata
- Authors
- Guo, Q., Kelly, M., & Graham, C. H.
- Venue
- Ecological Modelling
- Publisher
- Elsevier BV
- Published
- 2005-02
- Volume
- 182
- Issue
- 1
- Pages
- 75-90
- ISSN
- 0304-3800 (ISSN-L)
- OpenAlex
- W2107108409
- Cited by
- 340
- References
- 45
- FWCI
- 8.62
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Support vector machines for predicting distribution of Sudden Oak Death in California. https://app.cochat.ai/papers/10.1016/j.ecolmodel.2004.07.012
- The paper
- Guo, Q., Kelly, M., & Graham, C. H. (2005). Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling. https://doi.org/10.1016/j.ecolmodel.2004.07.012
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
