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Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks

Overview of attention for article published in Frontiers in Environmental Science, May 2023
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  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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4 X users

Citations

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8 Dimensions

Readers on

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8 Mendeley
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Title
Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks
Published in
Frontiers in Environmental Science, May 2023
DOI 10.3389/fenvs.2023.1187658
Authors

Hamid Gholami, Aliakbar Mohammadifar, Kathryn E. Fitzsimmons, Yue Li, Dimitris G. Kaskaoutis

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 13%
Professor > Associate Professor 1 13%
Student > Bachelor 1 13%
Unknown 5 63%
Readers by discipline Count As %
Engineering 2 25%
Earth and Planetary Sciences 1 13%
Unknown 5 63%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 May 2023.
All research outputs
#14,989,859
of 24,241,559 outputs
Outputs from Frontiers in Environmental Science
#969
of 4,177 outputs
Outputs of similar age
#183,394
of 379,012 outputs
Outputs of similar age from Frontiers in Environmental Science
#41
of 293 outputs
Altmetric has tracked 24,241,559 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,177 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 75% of its peers.
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 379,012 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 50% of its contemporaries.
We're also able to compare this research output to 293 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.