Evidence map›Paper›PMID 40161333›Full record

ArticlePeerJ2025

Enhancing the classification of isolated theropod teeth using machine learning: a comparative study.

Carolina S Marques, Emmanuel Dufourq, Soraia Pereira, Vanda F Santos, Elisabete Malafaia

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Carolina S MarquesCentro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
Emmanuel DufourqAfrican Institute for Mathematical Sciences, Muizenberg, South Africa.
Soraia PereiraCentro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
Vanda F SantosDepartamento de Geologia, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
Elisabete MalafaiaInstituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Classifying objects, such as taxonomic identification of fossils based on morphometric variables, is a time-consuming process. This task is further complicated by intra-class variability, which makes it ideal for automation via machine learning (ML) techniques. In this study, we compared six different ML techniques based on datasets with morphometric features used to classify isolated theropod teeth at both genus and higher taxonomic levels. Our model also intends to differentiate teeth from different positions on the tooth row (

Indexed as

DinosaursFossilsMachine LearningToothAnimalsAutomatic classificationMachine learningOversamplingStandardizationTheropoda

Identifiers

PMID40161333
PMCPMC11954464

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.