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Which of Our Modeling Predictions Are Robust?

Overview of attention for article published in PLoS Computational Biology, July 2012
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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.

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Mendeley readers

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%