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Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

Overview of attention for article published in Plant Methods, January 2022
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
18 Mendeley
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Title
Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature
Published in
Plant Methods, January 2022
DOI 10.1186/s13007-022-00839-5
Authors

Chaoxin Wang, Doina Caragea, Nisarga Kodadinne Narayana, Nathan T. Hein, Raju Bheemanahalli, Impa M. Somayanda, S. V. Krishna Jagadish

Twitter Demographics

The data shown below were collected from the profiles of 8 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 11%
Student > Master 2 11%
Other 1 6%
Lecturer 1 6%
Student > Bachelor 1 6%
Other 3 17%
Unknown 8 44%
Readers by discipline Count As %
Computer Science 4 22%
Agricultural and Biological Sciences 4 22%
Veterinary Science and Veterinary Medicine 1 6%
Engineering 1 6%
Unknown 8 44%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 26 January 2022.
All research outputs
#5,461,767
of 21,479,159 outputs
Outputs from Plant Methods
#299
of 1,029 outputs
Outputs of similar age
#108,113
of 389,637 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 21,479,159 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,029 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 70% 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 389,637 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% 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