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Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Overview of attention for article published in Journal of Computational Mathematics and Data Science, February 2024
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Article details
Title
Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models
Published in
Journal of Computational Mathematics and Data Science, February 2024
DOI 10.1016/j.jcmds.2024.100091
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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 February 2024.
All research outputs
#17,732,227
of 25,992,468 outputs
Outputs from Journal of Computational Mathematics and Data Science
#1
of 1 outputs
Outputs of similar age
#191,639
of 360,282 outputs
Outputs of similar age from Journal of Computational Mathematics and Data Science
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.0. This one scored the same or higher as 0 of them.
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 360,282 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
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