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AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements

Overview of attention for article published in Movement Ecology, December 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users
q&a
1 Q&A thread

Citations

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130 Dimensions

Readers on

mendeley
188 Mendeley
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Title
AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements
Published in
Movement Ecology, December 2014
DOI 10.1186/s40462-014-0027-0
Pubmed ID
Authors

Yehezkel S Resheff, Shay Rotics, Roi Harel, Orr Spiegel, Ran Nathan

Abstract

The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Israel 1 <1%
Spain 1 <1%
United States 1 <1%
Germany 1 <1%
Unknown 184 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 27%
Student > Master 35 19%
Researcher 33 18%
Student > Bachelor 14 7%
Student > Doctoral Student 12 6%
Other 19 10%
Unknown 25 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 98 52%
Environmental Science 30 16%
Computer Science 6 3%
Engineering 4 2%
Medicine and Dentistry 3 2%
Other 13 7%
Unknown 34 18%
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 06 December 2018.
All research outputs
#6,788,263
of 22,793,427 outputs
Outputs from Movement Ecology
#202
of 314 outputs
Outputs of similar age
#92,686
of 353,259 outputs
Outputs of similar age from Movement Ecology
#5
of 8 outputs
Altmetric has tracked 22,793,427 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 314 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.3. This one is in the 34th percentile – i.e., 34% 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 353,259 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 73% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.