Evidence map›Paper›PMID 36164481›Full record

ArticleMammalian biology = Zeitschrift fur Saugetierkunde2022

Multispecies facial detection for individual identification of wildlife: a case study across ursids.

Melanie Clapham, Ed Miller, Mary Nguyen, Russell C Van Horn

Abstract read
In one paragraph

Article in Mammalian biology = Zeitschrift fur Saugetierkunde, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

4 authors.

Melanie ClaphamDepartment of Geography, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2 Canada.ORCID 0000-0001-8924-7293
Ed MillerBearID Project, Sooke, BC Canada.ORCID 0000-0001-5257-6552
Mary NguyenBearID Project, Sooke, BC Canada.
Russell C Van HornSan Diego Zoo Wildlife Alliance, San Diego, CA USA.ORCID 0000-0002-5789-8822

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To address biodiversity decline in the era of big data, replicable methods of data processing are needed. Automated methods of individual identification (ID) via computer vision are valuable in conservation research and wildlife management. Rapid and systematic methods of image processing and analysis are fundamental to an ever-growing need for effective conservation research and practice. Bears (ursids) are an interesting test system for examining computer vision techniques for wildlife, as they have variable facial morphology, variable presence of individual markings, and are challenging to research and monitor. We leveraged existing imagery of bears living under human care to develop a multispecies bear face detector, a critical part of individual ID pipelines. We compared its performance across species and on a pre-existing wild brown bear Supplementary Information: The online version contains supplementary material available at 10.1007/s42991-021-00168-5.

Indexed as

BearsDeep learningFace recognitionIndividual IDMachine learningUrsidae

Identifiers

PMID36164481
PMCPMC9499902

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.