Title |
Heavy Tailed Approximate Identities and σ-stable Markov Kernels
|
---|---|
Published in |
Potential Analysis, July 2017
|
DOI | 10.1007/s11118-017-9644-8 |
Authors |
Hugo Aimar, Ivana Gómez, Federico Morana |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 4 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 14 July 2017.
All research outputs
#16,725,651
of 25,382,440 outputs
Outputs from Potential Analysis
#21
of 129 outputs
Outputs of similar age
#198,528
of 326,995 outputs
Outputs of similar age from Potential Analysis
#1
of 4 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 129 research outputs from this source. They receive a mean Attention Score of 1.0. This one has done well, scoring higher than 77% of its peers.
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 326,995 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 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