Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 329

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Euclidean distance matrix analysis: A coordinate‐free approach for comparing biological shapes using landmark data

Lele, Subhash, Richtsmeier, Joan T.·1991·American Journal of Physical Anthropology

Abstract

For problems of classification and comparison in biological research, the primary focus is on the similarity of forms. A biological form can be conveniently defined as consisting of size and shape. Several approaches for comparing biological shapes using landmark data are available. Lele (1991a) critically discusses these approaches and proposes a new method based on the Euclidean distance matrix representation of the form of an object. The purpose of this paper is to extend this new methodology to the comparison of groups of objects. We develop the statistical versions of various concepts introduced by Lele (1991a) and use them for developing statistical procedures for testing the hypothesis of shape difference between biological forms. We illustrate the use of this method by studying morphological differences between normal children and those affected with Crouzon and Apert syndromes and craniofacial sexual dimorphism in Cebus apella.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/ajpa.1330860307

Cites

21 of 21 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 21 references

Cited by

See all 329

Metadata

Authors
Lele, S., & Richtsmeier, J. T.
Venue
American Journal of Physical Anthropology
Publisher
Wiley
Published
1991-11
Volume
86
Issue
3
Pages
415-427
ISSN
  • 0002-9483 (ISSN-L)
  • 1096-8644
OpenAlex
W2058884235
Cited by
329
References
21
FWCI
12.41
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: Euclidean distance matrix analysis: A coordinate‐free approach for comparing biological shapes using landmark data. https://app.cochat.ai/papers/10.1002/ajpa.1330860307
The paper
Lele, S., & Richtsmeier, J. T. (1991). Euclidean distance matrix analysis: A coordinate‐free approach for comparing biological shapes using landmark data. American Journal of Physical Anthropology. https://doi.org/10.1002/ajpa.1330860307

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.