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Deep learning with self-supervision and uncertainty regularization to count fish in underwater images

Overview of attention for article published in PLOS ONE, May 2022
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
twitter
8 X users

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
53 Mendeley
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Title
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images
Published in
PLOS ONE, May 2022
DOI 10.1371/journal.pone.0267759
Pubmed ID
Authors

Penny Tarling, Mauricio Cantor, Albert Clapés, Sergio Escalera

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 9%
Other 5 9%
Student > Bachelor 4 8%
Student > Ph. D. Student 4 8%
Student > Doctoral Student 2 4%
Other 7 13%
Unknown 26 49%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 9%
Environmental Science 5 9%
Engineering 4 8%
Unspecified 3 6%
Computer Science 3 6%
Other 6 11%
Unknown 27 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 04 May 2022.
All research outputs
#2,475,530
of 24,093,053 outputs
Outputs from PLOS ONE
#30,996
of 207,132 outputs
Outputs of similar age
#55,643
of 429,736 outputs
Outputs of similar age from PLOS ONE
#639
of 4,777 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 207,132 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.6. This one has done well, scoring higher than 84% 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 429,736 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 4,777 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.