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Attention Score in Context
| Title |
Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas
|
|---|---|
| Published in |
Journal of Applied Remote Sensing, March 2024
|
| DOI | 10.1117/1.jrs.18.014522 |
| Authors |
Jae-yeop Kwon, Hye-kyeong Shin, Da-hui Kim, Hyeon-gyu Lee, Jin-kwang Bouk, Jung-hyun Kim, Tae-ho Kim |
Attention Score in Context
This research output has an Altmetric Attention Score of 53. 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 March 2024.
All research outputs
#801,015
of 25,643,886 outputs
Outputs from Journal of Applied Remote Sensing
#12
of 432 outputs
Outputs of similar age
#8,277
of 242,325 outputs
Outputs of similar age from Journal of Applied Remote Sensing
#1
of 3 outputs
Altmetric has tracked 25,643,886 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 432 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 97% 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 242,325 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 96% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them