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Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features

Overview of attention for article published in Plant Methods, September 2021
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Mentioned by

twitter
1 tweeter

Citations

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5 Dimensions

Readers on

mendeley
10 Mendeley
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Title
Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features
Published in
Plant Methods, September 2021
DOI 10.1186/s13007-021-00796-5
Authors

Xingchen Lin, Jianjun Chen, Peiqing Lou, Shuhua Yi, Yu Qin, Haotian You, Xiaowen Han

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Student > Master 1 10%
Researcher 1 10%
Unknown 6 60%
Readers by discipline Count As %
Environmental Science 1 10%
Biochemistry, Genetics and Molecular Biology 1 10%
Agricultural and Biological Sciences 1 10%
Engineering 1 10%
Unknown 6 60%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 September 2021.
All research outputs
#17,831,434
of 22,053,184 outputs
Outputs from Plant Methods
#934
of 1,049 outputs
Outputs of similar age
#256,272
of 350,534 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 22,053,184 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,049 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 350,534 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them