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Examining the Effect of Physicochemical and Meteorological Variables on Water Quality Indicators of Harmful Algal Blooms in a Shallow Hypereutrophic Lake Using Machine Learning Techniques

Overview of attention for article published in ACS ES&T Water, January 2024
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Mentioned by

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1 X user

Citations

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3 Dimensions

Readers on

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9 Mendeley
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Title
Examining the Effect of Physicochemical and Meteorological Variables on Water Quality Indicators of Harmful Algal Blooms in a Shallow Hypereutrophic Lake Using Machine Learning Techniques
Published in
ACS ES&T Water, January 2024
DOI 10.1021/acsestwater.3c00299
Authors

Susan A. Wherry, Liam Schenk

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 33%
Professor > Associate Professor 1 11%
Unknown 5 56%
Readers by discipline Count As %
Environmental Science 2 22%
Chemistry 1 11%
Unknown 6 67%
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 03 February 2024.
All research outputs
#17,732,227
of 25,992,468 outputs
Outputs from ACS ES&T Water
#1
of 1 outputs
Outputs of similar age
#193,489
of 362,290 outputs
Outputs of similar age from ACS ES&T Water
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.0. This one scored the same or higher as 0 of them.
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 362,290 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them