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Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale

Overview of attention for article published in Frontiers in Environmental Science, February 2022
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1 X user

Citations

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2 Dimensions

Readers on

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32 Mendeley
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Title
Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale
Published in
Frontiers in Environmental Science, February 2022
DOI 10.3389/fenvs.2022.812648
Authors

Yang Su, Huang Zhang, Benoit Gabrielle, David Makowski

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

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 22%
Student > Ph. D. Student 6 19%
Student > Bachelor 2 6%
Other 1 3%
Unspecified 1 3%
Other 3 9%
Unknown 12 38%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 22%
Engineering 3 9%
Environmental Science 2 6%
Computer Science 2 6%
Unspecified 1 3%
Other 3 9%
Unknown 14 44%
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 February 2022.
All research outputs
#20,522,137
of 23,090,520 outputs
Outputs from Frontiers in Environmental Science
#2,005
of 3,389 outputs
Outputs of similar age
#418,574
of 508,950 outputs
Outputs of similar age from Frontiers in Environmental Science
#175
of 360 outputs
Altmetric has tracked 23,090,520 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 3,389 research outputs from this source. They receive a mean Attention Score of 4.7. 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 508,950 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 360 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.