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Attention Score in Context
Title |
Which of Our Modeling Predictions Are Robust?
|
---|---|
Published in |
PLoS Computational Biology, July 2012
|
DOI | 10.1371/journal.pcbi.1002593 |
Pubmed ID | |
Authors |
Rob J. De Boer |
Abstract |
In theoretical ecology it is well known that the steady state expressions of the variables in a food chain crucially depend on the parity of the length of the chain. This poses a major problem for modeling real food webs because it is difficult to establish their true number of trophic levels, with sometimes rare predators and often rampant pathogens. Similar problems arise in the modeling of chronic viral infections. We review examples where seemingly general interpretations strongly depend on the number of levels in a model, and on its specific equations. This Perspective aims to open the discussion on this problem. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 20% |
United Kingdom | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 60% |
Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 13 | 13% |
Netherlands | 2 | 2% |
Portugal | 2 | 2% |
Spain | 2 | 2% |
Switzerland | 1 | 1% |
France | 1 | 1% |
Brazil | 1 | 1% |
India | 1 | 1% |
United Kingdom | 1 | 1% |
Other | 5 | 5% |
Unknown | 70 | 71% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 35 | 35% |
Student > Ph. D. Student | 30 | 30% |
Student > Doctoral Student | 6 | 6% |
Professor > Associate Professor | 6 | 6% |
Student > Master | 4 | 4% |
Other | 12 | 12% |
Unknown | 6 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 41 | 41% |
Biochemistry, Genetics and Molecular Biology | 11 | 11% |
Mathematics | 10 | 10% |
Computer Science | 7 | 7% |
Physics and Astronomy | 5 | 5% |
Other | 17 | 17% |
Unknown | 8 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 30 July 2012.
All research outputs
#8,535,684
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#5,638
of 8,960 outputs
Outputs of similar age
#60,977
of 178,783 outputs
Outputs of similar age from PLoS Computational Biology
#60
of 110 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 33rd percentile – i.e., 33% 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 178,783 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.