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Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

Overview of attention for article published in Plant Methods, June 2020
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 894)
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

news
7 news outlets
blogs
3 blogs
twitter
45 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
49 Mendeley
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Title
Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)
Published in
Plant Methods, June 2020
DOI 10.1186/s13007-020-00625-1
Pubmed ID
Authors

Michael Gomez Selvaraj, Manuel Valderrama, Diego Guzman, Milton Valencia, Henry Ruiz, Animesh Acharjee

Twitter Demographics

The data shown below were collected from the profiles of 45 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 16%
Researcher 6 12%
Student > Ph. D. Student 5 10%
Student > Doctoral Student 4 8%
Student > Bachelor 3 6%
Other 6 12%
Unknown 17 35%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 22%
Engineering 7 14%
Computer Science 6 12%
Environmental Science 4 8%
Medicine and Dentistry 1 2%
Other 3 6%
Unknown 17 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 101. 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 01 June 2021.
All research outputs
#265,249
of 18,772,620 outputs
Outputs from Plant Methods
#5
of 894 outputs
Outputs of similar age
#9,313
of 299,296 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 18,772,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 894 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done particularly well, scoring higher than 99% 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 299,296 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 96% of its contemporaries.
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