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Exploring Bayesian network model with noise filtering for rainfall-induced landslide susceptibility assessment in Fujian, China

Overview of attention for article published in Frontiers in Earth Science, August 2024
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Title
Exploring Bayesian network model with noise filtering for rainfall-induced landslide susceptibility assessment in Fujian, China
Published in
Frontiers in Earth Science, August 2024
DOI 10.3389/feart.2024.1444882
Authors

Suhua Zhou, Jinfeng Li, Jiuchang Zhang, Zhiwen Xu, Xianzhui Lu

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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 29 August 2024.
All research outputs
#21,626,156
of 26,547,438 outputs
Outputs from Frontiers in Earth Science
#2,983
of 6,378 outputs
Outputs of similar age
#83,815
of 133,334 outputs
Outputs of similar age from Frontiers in Earth Science
#27
of 79 outputs
Altmetric has tracked 26,547,438 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,378 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 33rd percentile – i.e., 33% 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 133,334 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.