Evidence map›Paper›PMID 38357591›Full record

ArticleEcology and evolution2024

Individual recognition of Eurasian beavers (

Margarete Dytkowicz, Marcello Tania, Rachel Hinds, William M Megill, Tillmann K Buttschardt, Frank Rosell

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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

6 authors at 2 institutions in 2 countries.

Margarete DytkowiczFabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve Germany.ORCID https://orcid.org/0009-0007-0818-9783
Marcello TaniaFabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve Germany.
Rachel HindsDepartment of Natural Sciences and Environmental Health, Faculty of Technology, Natural Sciences and Maritime Sciences University of South-Eastern Norway Bø i Telemark Norway.
William M MegillFabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve Germany.
Tillmann K ButtschardtResearch Group Applied Landscape Ecology and Ecological Planning, Institute of Landscape Ecology WWU Münster Münster Germany.
Frank RosellDepartment of Natural Sciences and Environmental Health, Faculty of Technology, Natural Sciences and Maritime Sciences University of South-Eastern Norway Bø i Telemark Norway.
Rhine-Waal University of Applied Sciences · DEUniversity of South-Eastern Norway · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (

Indexed as

Castor fiberEurasian beaverindividual pattern recognitionnon‐invasive mark–recapturescale‐invariant feature transformwildlife ecology

Identifiers

PMID38357591
PMCPMC10864685
OpenAlexW4391818666

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.