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Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases

Overview of attention for article published in Frontiers in Public Health, July 2024
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Title
Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases
Published in
Frontiers in Public Health, July 2024
DOI 10.3389/fpubh.2024.1420608
Authors

Hui Xu, Shufang Guo, Xiaojun Shi, Yanzhen Wu, Junyi Pan, Han Gao, Yan Tang, Aiqing Han

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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 22 July 2024.
All research outputs
#23,704,888
of 26,381,372 outputs
Outputs from Frontiers in Public Health
#10,682
of 14,742 outputs
Outputs of similar age
#105,148
of 133,379 outputs
Outputs of similar age from Frontiers in Public Health
#112
of 124 outputs
Altmetric has tracked 26,381,372 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,742 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 1st percentile – i.e., 1% 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 133,379 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.