↓ Skip to main content

A vertically non-uniform temperature approach for the friction term computation in depth-averaged viscoplastic lava flows

Overview of attention for article published in Journal of Computational Physics, December 2024
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
4 X users
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A vertically non-uniform temperature approach for the friction term computation in depth-averaged viscoplastic lava flows
Published in
Journal of Computational Physics, December 2024
DOI 10.1016/j.jcp.2024.113378
Authors

J. Ortega-Moya, S. Martínez-Aranda, J. Fernández-Pato, P. García-Navarro

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
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 04 September 2024.
All research outputs
#8,906,385
of 26,588,416 outputs
Outputs from Journal of Computational Physics
#584
of 5,859 outputs
Outputs of similar age
#1,738
of 4,242 outputs
Outputs of similar age from Journal of Computational Physics
#1
of 11 outputs
Altmetric has tracked 26,588,416 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 5,859 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done well, scoring higher than 89% 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 4,242 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 59% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.