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Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Overview of attention for article published in Ecology and Evolution, September 2020
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
2 news outlets
policy
2 policy sources
twitter
69 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
79 Mendeley
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Title
Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2
Published in
Ecology and Evolution, September 2020
DOI 10.1002/ece3.6692
Pubmed ID
Authors

Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric A. Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. VerCauteren, Jeff Clune, Ryan S. Miller

Twitter Demographics

The data shown below were collected from the profiles of 69 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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 22%
Student > Ph. D. Student 12 15%
Student > Master 10 13%
Student > Bachelor 8 10%
Student > Doctoral Student 5 6%
Other 7 9%
Unknown 20 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 32%
Environmental Science 14 18%
Computer Science 4 5%
Psychology 2 3%
Unspecified 2 3%
Other 9 11%
Unknown 23 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 64. 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 07 April 2022.
All research outputs
#501,695
of 21,078,563 outputs
Outputs from Ecology and Evolution
#189
of 6,811 outputs
Outputs of similar age
#15,250
of 325,380 outputs
Outputs of similar age from Ecology and Evolution
#9
of 224 outputs
Altmetric has tracked 21,078,563 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,811 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one has done particularly well, scoring higher than 97% 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 325,380 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 95% of its contemporaries.
We're also able to compare this research output to 224 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.