↓ Skip to main content

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Overview of attention for article published in Frontiers in Plant Science, December 2020
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
5 X users

Readers on

mendeley
80 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction
Published in
Frontiers in Plant Science, December 2020
DOI 10.3389/fpls.2020.590529
Pubmed ID
Authors

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 9 11%
Student > Master 6 8%
Other 4 5%
Lecturer 4 5%
Student > Bachelor 4 5%
Other 10 13%
Unknown 43 54%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 17 21%
Computer Science 8 10%
Engineering 5 6%
Biochemistry, Genetics and Molecular Biology 4 5%
Environmental Science 1 1%
Other 2 3%
Unknown 43 54%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 18 January 2021.
All research outputs
#16,268,804
of 27,645,983 outputs
Outputs from Frontiers in Plant Science
#6,798
of 25,775 outputs
Outputs of similar age
#263,219
of 534,709 outputs
Outputs of similar age from Frontiers in Plant Science
#202
of 534 outputs
Altmetric has tracked 27,645,983 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 25,775 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 72% 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 534,709 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 534 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 60% of its contemporaries.