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Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods

Overview of attention for article published in Frontiers in Water, March 2023
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Mentioned by

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2 X users

Citations

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6 Dimensions

Readers on

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12 Mendeley
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Title
Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods
Published in
Frontiers in Water, March 2023
DOI 10.3389/frwa.2023.1112970
Authors

Ming Fan, Lujun Zhang, Siyan Liu, Tiantian Yang, Dan Lu

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 17%
Other 1 8%
Lecturer 1 8%
Professor > Associate Professor 1 8%
Student > Bachelor 1 8%
Other 3 25%
Unknown 3 25%
Readers by discipline Count As %
Engineering 5 42%
Environmental Science 2 17%
Earth and Planetary Sciences 1 8%
Unknown 4 33%
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 21 March 2023.
All research outputs
#19,015,492
of 23,567,572 outputs
Outputs from Frontiers in Water
#345
of 569 outputs
Outputs of similar age
#253,071
of 369,791 outputs
Outputs of similar age from Frontiers in Water
#19
of 43 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 569 research outputs from this source. They receive a mean Attention Score of 4.7. 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 369,791 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.