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Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Overview of attention for article published in Data-Centric Engineering, November 2022
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About this Attention Score

  • Among the highest-scoring outputs from this source (#41 of 146)
  • Above-average Attention Score compared to outputs of the same age (64th percentile)

Mentioned by

twitter
4 X users

Readers on

mendeley
17 Mendeley
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Article details
Title
Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning
Published in
Data-Centric Engineering, November 2022
DOI 10.1017/dce.2022.32
Authors

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Timeline Attention over time Attention Score history
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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.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 3 18%
Student > Ph. D. Student 1 6%
Other 1 6%
Unknown 12 71%
Readers by discipline
Readers by discipline Count As %
Engineering 3 18%
Earth and Planetary Sciences 1 6%
Energy 1 6%
Design 1 6%
Unknown 11 65%
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 03 December 2022.
All research outputs
#11,308,544
of 32,349,805 outputs
Outputs from Data-Centric Engineering
#41
of 146 outputs
Outputs of similar age
#158,970
of 457,035 outputs
Outputs of similar age from Data-Centric Engineering
#5
of 7 outputs
Altmetric has tracked 32,349,805 research outputs across all sources so far. This one has received more attention than most of these and is in the 64th percentile.
So far Altmetric has tracked 146 research outputs from this source. They receive a mean Attention Score of 3.0. This one has gotten more attention than average, scoring higher than 71% 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 457,035 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 64% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.