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A Machine Learning Based Hybrid Multi-Fidelity Multi-Level Monte Carlo Method for Uncertainty Quantification

Overview of attention for article published in Frontiers in Environmental Science, August 2019
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2 X users

Citations

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6 Dimensions

Readers on

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19 Mendeley
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Title
A Machine Learning Based Hybrid Multi-Fidelity Multi-Level Monte Carlo Method for Uncertainty Quantification
Published in
Frontiers in Environmental Science, August 2019
DOI 10.3389/fenvs.2019.00105
Authors

Nagoor Kani Jabarullah Khan, Ahmed H. Elsheikh

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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Lecturer 2 11%
Unspecified 1 5%
Student > Bachelor 1 5%
Other 1 5%
Other 2 11%
Unknown 7 37%
Readers by discipline Count As %
Engineering 3 16%
Chemical Engineering 2 11%
Earth and Planetary Sciences 2 11%
Materials Science 2 11%
Agricultural and Biological Sciences 1 5%
Other 3 16%
Unknown 6 32%
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 18 September 2019.
All research outputs
#18,026,894
of 23,154,520 outputs
Outputs from Frontiers in Environmental Science
#1,281
of 3,450 outputs
Outputs of similar age
#238,259
of 340,346 outputs
Outputs of similar age from Frontiers in Environmental Science
#30
of 41 outputs
Altmetric has tracked 23,154,520 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,450 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 59% 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 340,346 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.