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Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

Overview of attention for article published in Environmental Research Letters (ERL), December 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

twitter
40 tweeters

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
41 Mendeley
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Title
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Published in
Environmental Research Letters (ERL), December 2020
DOI 10.1088/1748-9326/abd501
Authors

Farshid Rahmani, Kathryn Lawson, Wenyu Ouyang, Alison Appling, Samantha Oliver, Chaopeng Shen

Twitter Demographics

The data shown below were collected from the profiles of 40 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 32%
Researcher 7 17%
Student > Master 6 15%
Student > Doctoral Student 3 7%
Professor > Associate Professor 2 5%
Other 4 10%
Unknown 6 15%
Readers by discipline Count As %
Environmental Science 12 29%
Engineering 7 17%
Agricultural and Biological Sciences 4 10%
Earth and Planetary Sciences 3 7%
Computer Science 2 5%
Other 1 2%
Unknown 12 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 August 2022.
All research outputs
#1,157,144
of 21,980,322 outputs
Outputs from Environmental Research Letters (ERL)
#1,490
of 5,263 outputs
Outputs of similar age
#39,592
of 490,621 outputs
Outputs of similar age from Environmental Research Letters (ERL)
#65
of 180 outputs
Altmetric has tracked 21,980,322 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,263 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 48.3. This one has gotten more attention than average, scoring higher than 71% 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 490,621 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 180 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.