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Combining Weibull distribution and k-nearest neighbor imputation method to predict wall-to-wall tree lists for the entire forest region of Northeast China

Overview of attention for article published in Annals of Forest Science , October 2022
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
6 Mendeley
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Title
Combining Weibull distribution and k-nearest neighbor imputation method to predict wall-to-wall tree lists for the entire forest region of Northeast China
Published in
Annals of Forest Science , October 2022
DOI 10.1186/s13595-022-01161-9
Authors

Yuanyuan Fu, Hong S. He, Shaoqiang Wang, Lunche Wang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 17%
Researcher 1 17%
Student > Master 1 17%
Unknown 3 50%
Readers by discipline Count As %
Unspecified 1 17%
Environmental Science 1 17%
Agricultural and Biological Sciences 1 17%
Unknown 3 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 October 2022.
All research outputs
#6,604,508
of 25,392,582 outputs
Outputs from Annals of Forest Science
#473
of 942 outputs
Outputs of similar age
#119,487
of 439,982 outputs
Outputs of similar age from Annals of Forest Science
#1
of 9 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 942 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 26th percentile – i.e., 26% 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 439,982 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 9 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