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DevelNet: Earthquake Detection on Develocorder Films with Deep Learning: Application to the Rangely Earthquake Control Experiment

Overview of attention for article published in Seismological Research Letters, July 2022
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Mentioned by

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4 X users

Citations

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3 Dimensions

Readers on

mendeley
7 Mendeley
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Title
DevelNet: Earthquake Detection on Develocorder Films with Deep Learning: Application to the Rangely Earthquake Control Experiment
Published in
Seismological Research Letters, July 2022
DOI 10.1785/0220220066
Authors

Kaiwen Wang, William Ellsworth, Gregory C. Beroza, Weiqiang Zhu, Justin L. Rubinstein

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 readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Lecturer 1 14%
Other 1 14%
Student > Doctoral Student 1 14%
Unknown 1 14%
Readers by discipline Count As %
Earth and Planetary Sciences 5 71%
Unknown 2 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 July 2022.
All research outputs
#16,968,879
of 25,714,183 outputs
Outputs from Seismological Research Letters
#1,257
of 1,688 outputs
Outputs of similar age
#244,851
of 441,516 outputs
Outputs of similar age from Seismological Research Letters
#32
of 63 outputs
Altmetric has tracked 25,714,183 research outputs across all sources so far. This one is in the 31st percentile – i.e., 31% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,688 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 23rd percentile – i.e., 23% 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 441,516 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.