Evidence map›Paper›PMID 35942123›Full record

ArticlePeerJ2022

Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia.

Alice Hua, Kevin Martin, Yuzeng Shen, Nicole Chen, Catherine Mou, Maximilian Sterk, Berend Reinhard, Friedrich F Reinhard, Stephen Lee, Sky Alibhai and 1 more

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

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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

11 authors at 4 institutions in 2 countries.

Alice Hua *School of Information, University of California, Berkeley, Berkeley, California, USA.
Kevin Martin *School of Information, University of California, Berkeley, Berkeley, California, USA.
Yuzeng ShenSchool of Information, University of California, Berkeley, Berkeley, California, USA.ORCID 0000-0002-2125-3053
Nicole ChenSchool of Information, University of California, Berkeley, Berkeley, California, USA.
Catherine MouSchool of Information, University of California, Berkeley, Berkeley, California, USA.
Maximilian SterkDepartment of Conservation Biology, University of Göttingen, Göttingen, Germany.
Berend ReinhardKuzikus Wildlife Reserve, Windhoek, Omaheke, Namibia.
Friedrich F ReinhardKuzikus Wildlife Reserve, Windhoek, Omaheke, Namibia.
Stephen LeeArmy Research Office, Durham, North Carolina, USA.
Sky AlibhaiNicholas School of the Environment, Duke University, Durham, North Carolina, USA.
Zoe C JewellNicholas School of the Environment, Duke University, Durham, North Carolina, USA.ORCID 0000-0002-4915-7952
University of California, Berkeley · USDuke University · USUnited States Army Research Office · USUniversity of Göttingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessing the numbers and distribution of at-risk megafauna such as the black rhino (

Indexed as

Artificial IntelligenceUnmanned Aerial DevicesHumansNamibiaAIDronesIoT devicesNamibiaNon-invasiveRemote area monitoringRemote sensingRhino monitoringUAVsYOLO

Identifiers

PMID35942123
PMCPMC9356584
OpenAlexW4289711195

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.