Addressing Concerns about Single Path Analysis in Business Cycle Turning Points: The Case of Learning Vector Quantization

Overview of attention for article published in Mathematics, February 2024

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peer_reviews: 1 peer review site

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Article details
Title	Addressing Concerns about Single Path Analysis in Business Cycle Turning Points: The Case of Learning Vector Quantization
Published in	Mathematics, February 2024
DOI	10.3390/math12050678
Authors	David Enck, Mario Beruvides, Víctor G. Tercero-Gómez, Alvaro E. Cordero-Franco

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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 15 January 2024.

All research outputs

#18,147,734

of 26,545,486 outputs

Outputs from Mathematics

#1,084

of 3,696 outputs

Outputs of similar age

#194,138

of 356,975 outputs

Outputs of similar age from Mathematics

#26

of 107 outputs

Altmetric has tracked 26,545,486 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.

So far Altmetric has tracked 3,696 research outputs from this source. They receive a mean Attention Score of 1.8. This one is in the 37th percentile – i.e., 37% 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 356,975 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.

We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.