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Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach

Overview of attention for article published in Frontiers in Genetics, May 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
5 X users
facebook
2 Facebook pages

Citations

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17 Dimensions

Readers on

mendeley
42 Mendeley
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Title
Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach
Published in
Frontiers in Genetics, May 2020
DOI 10.3389/fgene.2020.00513
Pubmed ID
Authors

Jessica Nye, Laura M. Zingaretti, Miguel Pérez-Enciso

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 7 17%
Student > Bachelor 3 7%
Professor > Associate Professor 3 7%
Student > Master 2 5%
Other 3 7%
Unknown 13 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 45%
Computer Science 4 10%
Biochemistry, Genetics and Molecular Biology 3 7%
Veterinary Science and Veterinary Medicine 2 5%
Unspecified 1 2%
Other 0 0%
Unknown 13 31%
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 06 June 2020.
All research outputs
#7,982,504
of 24,022,746 outputs
Outputs from Frontiers in Genetics
#2,621
of 12,901 outputs
Outputs of similar age
#161,910
of 393,491 outputs
Outputs of similar age from Frontiers in Genetics
#82
of 371 outputs
Altmetric has tracked 24,022,746 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,901 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 79% 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 393,491 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 54% of its contemporaries.
We're also able to compare this research output to 371 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.