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The importance of training population size and heritability on the accuracies of GEBV for continuous phenotypes [ 2, 31] and ordinal categorical phenotypes [ 23] has been shown in simulation studies.
In fact, coalescence theory predicts that in some cases, the most probable gene tree will not even reflect the species tree [ 9, 10], and this has also been shown in simulation studies using coalescent models on concatenated data [ 11].
This is likely to be a substantially harder and more complex endeavor, however, since the most commonly used approach to addressing post-initiation confounding, generating a "pseudopopulation", usually occurs by weighting, which has not been shown in simulation to produce very predictable confounding amplification.
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Some of these variants have been shown in simulations to provide delivery rates that are even closer to that of simple flooding while still using far less messages.
Interestingly, such strategies have been shown in simulations not to be evolutionarily stable, but to catalyze the evolution of cooperation in populations (Hilbe et al., 2013).
This is shown in simulation experiments, which are described in the paper.
The effectiveness of the approach is shown in simulation on an appropriate ITER configuration.
This result was shown in simulation only, for a shape memory alloy actuator.
The stability of the controller is shown in simulations using a diptera insect model.
Actually, the value of n t does not need to be too large as will be shown in simulations.
It is shown in simulations to exhibit superior positioning performance and robustness compared to model-based friction compensation methods.
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