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Evaluating the predictive power of machine learning model for shear transformation in metallic glasses using metrics for an imbalanced dataset

Overview of attention for article published in Frontiers in Materials, July 2022
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About this Attention Score

  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

twitter
2 X users

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
4 Mendeley
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Title
Evaluating the predictive power of machine learning model for shear transformation in metallic glasses using metrics for an imbalanced dataset
Published in
Frontiers in Materials, July 2022
DOI 10.3389/fmats.2022.874339
Authors

Jaemin Lee, Seunghwa Ryu

Timeline

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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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 25%
Unknown 3 75%
Readers by discipline Count As %
Computer Science 1 25%
Unknown 3 75%
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 23 July 2022.
All research outputs
#18,936,064
of 24,132,754 outputs
Outputs from Frontiers in Materials
#504
of 2,773 outputs
Outputs of similar age
#291,837
of 421,663 outputs
Outputs of similar age from Frontiers in Materials
#17
of 179 outputs
Altmetric has tracked 24,132,754 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,773 research outputs from this source. They receive a mean Attention Score of 1.6. This one has done well, scoring higher than 76% 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 421,663 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 179 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.