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Editorial: Applications of Machine Learning to Evolutionary Ecology Data

Overview of attention for article published in Frontiers in Ecology and Evolution, November 2021
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
7 X users

Readers on

mendeley
6 Mendeley
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Title
Editorial: Applications of Machine Learning to Evolutionary Ecology Data
Published in
Frontiers in Ecology and Evolution, November 2021
DOI 10.3389/fevo.2021.797319
Authors

Juliano Morimoto, Aurore Ponchon, Georgy Sofronov, Justin Travis

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 17%
Student > Bachelor 1 17%
Lecturer 1 17%
Unknown 3 50%
Readers by discipline Count As %
Mathematics 1 17%
Agricultural and Biological Sciences 1 17%
Unknown 4 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 December 2021.
All research outputs
#6,667,283
of 22,660,862 outputs
Outputs from Frontiers in Ecology and Evolution
#1,681
of 4,013 outputs
Outputs of similar age
#145,664
of 496,435 outputs
Outputs of similar age from Frontiers in Ecology and Evolution
#96
of 281 outputs
Altmetric has tracked 22,660,862 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 4,013 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.0. This one has gotten more attention than average, scoring higher than 57% 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 496,435 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 70% of its contemporaries.
We're also able to compare this research output to 281 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.