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Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling

Overview of attention for article published in Journal of Big Data, February 2021
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Mentioned by

twitter
1 tweeter

Citations

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3 Dimensions

Readers on

mendeley
27 Mendeley
Title
Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling
Published in
Journal of Big Data, February 2021
DOI 10.1186/s40537-021-00417-x
Authors

Minjung Ryu, Hong-Linh Truong, Matti Kannala

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 19%
Student > Doctoral Student 4 15%
Student > Master 2 7%
Professor 2 7%
Student > Ph. D. Student 2 7%
Other 1 4%
Unknown 11 41%
Readers by discipline Count As %
Engineering 10 37%
Business, Management and Accounting 3 11%
Computer Science 2 7%
Arts and Humanities 1 4%
Design 1 4%
Other 0 0%
Unknown 10 37%

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 16 February 2021.
All research outputs
#14,163,614
of 17,740,717 outputs
Outputs from Journal of Big Data
#194
of 267 outputs
Outputs of similar age
#226,058
of 306,775 outputs
Outputs of similar age from Journal of Big Data
#1
of 1 outputs
Altmetric has tracked 17,740,717 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one is in the 3rd percentile – i.e., 3% 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 306,775 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 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