Evidence map›Paper›PMID 39035157›Full record

ArticlePeerJ2024

Classification of major species in the

Wenhao Liu, Wanqiang Han, Guili Jin, Ke Gong, Jian Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wenhao Liu *College of Grassland Sciences of Xinjiang Agricultural University, Xinjiang Agriculture University, Urumqi, Xinjiang, China.
Wanqiang Han *College of Grassland Sciences of Xinjiang Agricultural University, Xinjiang Agriculture University, Urumqi, Xinjiang, China.
Guili JinCollege of Grassland Sciences of Xinjiang Agricultural University, Xinjiang Agriculture University, Urumqi, Xinjiang, China.
Ke GongCollege of Grassland Sciences of Xinjiang Agricultural University, Xinjiang Agriculture University, Urumqi, Xinjiang, China.
Jian MaCollege of Grassland Sciences of Xinjiang Agricultural University, Xinjiang Agriculture University, Urumqi, Xinjiang, China.

Funding

National Natural Science Foundation of ChinaOpen Project of Key Laboratory in Xinjiang Uygur Autonomous Region
6 · The paper itself

Abstract

Background: The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring. Methods: Ground hyperspectral images of a Results: The selection of parameters with significant differences ( Conclusions: The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in

Indexed as

Desert ClimateGrasslandArtemisiaChinaDiscriminant AnalysisHyperspectral ImagingRemote Sensing TechnologySeasonsFisher discriminantGrassland plantsIdentification parametersScreeningSpectral characteristics

Identifiers

PMID39035157
PMCPMC11260411

What OpenQuestion holds

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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.