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Exploring the capabilities and limitations of large language models in the electric energy sector

Overview of attention for article published in Joule, June 2024
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

news
9 news outlets

Readers on

mendeley
5 Mendeley
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Title
Exploring the capabilities and limitations of large language models in the electric energy sector
Published in
Joule, June 2024
DOI 10.1016/j.joule.2024.05.009
Authors

Subir Majumder, Lin Dong, Fatemeh Doudi, Yuting Cai, Chao Tian, Dileep Kalathil, Kevin Ding, Anupam A. Thatte, Na Li, Le Xie

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Student > Master 1 20%
Unknown 2 40%
Readers by discipline Count As %
Engineering 2 40%
Computer Science 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 66. 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 26 June 2024.
All research outputs
#672,758
of 26,200,644 outputs
Outputs from Joule
#265
of 1,328 outputs
Outputs of similar age
#7,002
of 239,091 outputs
Outputs of similar age from Joule
#6
of 35 outputs
Altmetric has tracked 26,200,644 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,328 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 60.7. This one has done well, scoring higher than 80% 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 239,091 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.