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Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

Overview of attention for article published in Frontiers in Earth Science, April 2022
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1 X user

Citations

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7 Mendeley
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Title
Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm
Published in
Frontiers in Earth Science, April 2022
DOI 10.3389/feart.2022.884500
Authors

Guanwen Huang, Duo Wang, Yuan Du, Qin Zhang, Zhengwei Bai, Chun Wang

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X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 14%
Student > Master 1 14%
Unknown 5 71%
Readers by discipline Count As %
Unspecified 1 14%
Environmental Science 1 14%
Unknown 5 71%
Attention Score in Context

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 09 May 2022.
All research outputs
#20,712,517
of 23,312,088 outputs
Outputs from Frontiers in Earth Science
#3,171
of 4,869 outputs
Outputs of similar age
#362,469
of 443,185 outputs
Outputs of similar age from Frontiers in Earth Science
#254
of 426 outputs
Altmetric has tracked 23,312,088 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,869 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% 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 443,185 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 426 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.