Evidence map›Paper›PMID 36262418›Full record

ArticlePeerJ2022

Automated mapping of

Nicholas C Galuszynski, Robbert Duker, Alastair J Potts, Teja Kattenborn

Abstract 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 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. 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

4 authors.

Nicholas C Galuszynski *Department of Botany, Nelson Mandela University, Gqeberha, South Africa.ORCID 0000-0003-4595-3721
Robbert Duker *Department of Botany, Nelson Mandela University, Gqeberha, South Africa.
Alastair J PottsDepartment of Botany, Nelson Mandela University, Gqeberha, South Africa.ORCID 0000-0003-0919-7279
Teja KattenbornRemote Sensing Centre for Earth System Research (RSC4Earth), Universität Leipzig, Leipzig, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ecosystem restoration and reforestation often operate at large scales, whereas monitoring practices are usually limited to spatially restricted field measurements that are (i) time- and labour-intensive, and (ii) unable to accurately quantify restoration success over hundreds to thousands of hectares. Recent advances in remote sensing technologies paired with deep learning algorithms provide an unprecedented opportunity for monitoring changes in vegetation cover at spatial and temporal scales. Such data can feed directly into adaptive management practices and provide insights into restoration and regeneration dynamics. Here, we demonstrate that convolutional neural network (CNN) segmentation algorithms can accurately classify the canopy cover of

Indexed as

EcosystemUnmanned Aerial DevicesAlgorithmsNeural Networks, ComputerRemote Sensing TechnologyAdaptive managementAerial imageryAlbany subtropical thicketCNNDrone imageryEcosystem monitoringMachine learningRestoration ecologySpekboomUAVs

Identifiers

PMID36262418
PMCPMC9575683

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.