In cases where exposure data are obtained from different sources in parallel, for instance in our simulations of one exposure trace comprising two exposure realizations, C-EVA provides one single outcome describing overall exposure variation whereas the conventional EVA would provide one result per source of data.
For instance, in metacommunity simulations comparing indices of biodiversity, specialists declined rapidly with disturbance intensity while species richness and diversity were far less sensitive to changes in disturbance [ 3].
For instance, in our simulation, we can compare the results of scenario 2, for animals in generation 2 that have only phenotypes for trait 1 evaluated with multi-trait model A, with the results for animals with no phenotypes in generation 3 that were evaluated with the MT SNP-based models.
For instance, in a simulation that assumed that the top 10% of grants were funded, and that bias reduced the scores of nonpreferred applicants by an average of 3.7%, the preferred applicants got 118 funded grants, compared with just 82 from nonpreferred applicants.
For instance, in each run of simulation the concentration of CC can be assumed to be a random number generated based on a log-normal distribution around its mean concentration with a CV of 0.25.
For instance, in the simulation experiment on the iterative version with starting condition #3, when ∼2500 terms are curated, 3000 of 4000 GO+ terms in the test set have been found.
For instance, in the case of 10000 simulations, we achieved a noticeable 181× speedup.
For instance, in the absence of pERK-pFRS2 feedback loop, pERK levels increase according to our model simulations in response to FGFR activation and remain maximal until the receptor is internalized and degraded.
For instance, in early stages of a building lifecycle, energy efficiency figures may be rough estimates which need to be refined in later stages (for instance, when results of detailed simulations become available).
