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Scalable deep learning for watershed model calibration

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

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13 Mendeley
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Title
Scalable deep learning for watershed model calibration
Published in
Frontiers in Earth Science, November 2022
DOI 10.3389/feart.2022.1026479
Authors

Maruti K. Mudunuru, Kyongho Son, Peishi Jiang, Glenn Hammond, Xingyuan Chen

X Demographics

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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 15%
Unspecified 1 8%
Other 1 8%
Student > Master 1 8%
Researcher 1 8%
Other 1 8%
Unknown 6 46%
Readers by discipline Count As %
Environmental Science 3 23%
Unspecified 1 8%
Computer Science 1 8%
Agricultural and Biological Sciences 1 8%
Earth and Planetary Sciences 1 8%
Other 0 0%
Unknown 6 46%
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 December 2022.
All research outputs
#20,706,566
of 23,306,612 outputs
Outputs from Frontiers in Earth Science
#3,169
of 4,866 outputs
Outputs of similar age
#351,932
of 444,239 outputs
Outputs of similar age from Frontiers in Earth Science
#158
of 244 outputs
Altmetric has tracked 23,306,612 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 4,866 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 444,239 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 244 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.