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Editorial: Computational models of affordance for robotics

Overview of attention for article published in Frontiers in Neurorobotics, October 2022
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  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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1 X user

Citations

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1 Dimensions

Readers on

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1 Mendeley
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Title
Editorial: Computational models of affordance for robotics
Published in
Frontiers in Neurorobotics, October 2022
DOI 10.3389/fnbot.2022.1045355
Pubmed ID
Authors

Erwan Renaudo, Philipp Zech, Raja Chatila, Mehdi Khamassi

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Unspecified 1 100%
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 17 October 2022.
All research outputs
#15,843,787
of 23,543,207 outputs
Outputs from Frontiers in Neurorobotics
#474
of 919 outputs
Outputs of similar age
#241,714
of 442,667 outputs
Outputs of similar age from Frontiers in Neurorobotics
#14
of 60 outputs
Altmetric has tracked 23,543,207 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 919 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 42nd percentile – i.e., 42% 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 442,667 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 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.