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Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

Overview of attention for article published in Genetics Selection Evolution, February 2014
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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2 X users

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Title
Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models
Published in
Genetics Selection Evolution, February 2014
DOI 10.1186/1297-9686-46-16
Pubmed ID
Authors

Panya Sae-Lim, Hans Komen, Antti Kause, Han A Mulder

Abstract

Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 4%
France 1 1%
United States 1 1%
Unknown 63 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 24%
Student > Ph. D. Student 11 16%
Student > Master 11 16%
Student > Doctoral Student 4 6%
Professor 3 4%
Other 11 16%
Unknown 12 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 56%
Environmental Science 4 6%
Biochemistry, Genetics and Molecular Biology 2 3%
Unspecified 2 3%
Medicine and Dentistry 2 3%
Other 4 6%
Unknown 16 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 September 2014.
All research outputs
#16,048,009
of 25,374,917 outputs
Outputs from Genetics Selection Evolution
#480
of 822 outputs
Outputs of similar age
#128,705
of 235,082 outputs
Outputs of similar age from Genetics Selection Evolution
#5
of 14 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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 235,082 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 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 64% of its contemporaries.