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A Machine-Learning Based Tool for Diagnosing Inland Tropical Cyclone Maintenance or Intensification Events

Overview of attention for article published in Frontiers in Earth Science, March 2022
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

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1 X user

Citations

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2 Dimensions

Readers on

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4 Mendeley
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Title
A Machine-Learning Based Tool for Diagnosing Inland Tropical Cyclone Maintenance or Intensification Events
Published in
Frontiers in Earth Science, March 2022
DOI 10.3389/feart.2022.818671
Authors

Andrew Michael Thomas, James Marshall Shepherd

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 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 %
Unspecified 1 25%
Student > Ph. D. Student 1 25%
Researcher 1 25%
Unknown 1 25%
Readers by discipline Count As %
Unspecified 1 25%
Pharmacology, Toxicology and Pharmaceutical Science 1 25%
Physics and Astronomy 1 25%
Unknown 1 25%
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 03 March 2022.
All research outputs
#15,646,171
of 23,257,423 outputs
Outputs from Frontiers in Earth Science
#1,897
of 4,835 outputs
Outputs of similar age
#252,097
of 440,932 outputs
Outputs of similar age from Frontiers in Earth Science
#150
of 505 outputs
Altmetric has tracked 23,257,423 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,835 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 55% 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 440,932 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 505 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.