The DOI matches its Crossref record.
Some Thoughts on Bacterial Classification
Abstract
SUMMARY: Scientific classification is virtually a branch of mathematics which describes the overall similarities of organisms. Catalogues do not do this. Many schemes of bacterial taxonomy are not classifications but catalogues. Similarity is best measured by the number of features in common between two strains, while division into taxa is based on correlated features. Other criteria for these two basic ideas are unsatisfactory and cause confusion, and there seems to be no logical reason why any one feature should be given greater weight in classification than any other. Hierarchical systems are a practical necessity, and simple mathematical methods are useful in bacterial classification. It is not necessary to know the evolutionary history of organisms in order to classify them in a scientific manner.
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
Cited by
Distinguishing Between the Validity and Utility of Psychiatric Diagnoses
Kendell, R., & Jablensky, A. (2003) · American Journal of Psychiatry · doi:10.1176/appi.ajp.160.1.4 · Cited by 1,246
The History of the Cluster Heat Map
Wilkinson, L., & Friendly, M. (2009) · The American Statistician · doi:10.1198/tas.2009.0033 · Cited by 932
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766
Swings, J., & De Ley, J. (1977) · Bacteriological Reviews · doi:10.1128/br.41.1.1-46.1977 · Cited by 655
Metadata
- Authors
- Sneath, P. H. A.
- Venue
- Journal of General Microbiology
- Publisher
- Microbiology Society
- Published
- 1957-08-01
- Volume
- 17
- Issue
- 1
- Pages
- 184-200
- ISSN
- 0022-1287 (ISSN-L)
- OpenAlex
- W1963752964
- Cited by
- 319
- References
- 0
- FWCI
- 5.06
- 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: Some Thoughts on Bacterial Classification. https://app.cochat.ai/papers/10.1099/00221287-17-1-184
- The paper
- Sneath, P. H. A. (1957). Some Thoughts on Bacterial Classification. Journal of General Microbiology. https://doi.org/10.1099/00221287-17-1-184
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.
