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Each model run consisted of 100 iterations to ensure that the solution represents a global minimum.
Each model run consisted of a maximum of 1,000 iterations or until the convergence limit of 0.01 was reached.
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Each simulation run consisted of 200 replicates.
To ensure stable results each model combined twenty balanced random forest algorithm runs consisting of 10,000 individual classification trees.
For the Bayesian analyses, a relaxed or strict molecular clock model was used and each run consisted of two independent 100×10 Markov chain Monte Carlo generations with sampling every 10 000 generations and a constant or Yule coalescent tree prior.
A typical run consisted of nine iterations.
Each run consisted of 80 trials.
Each run consisted of a burn-in period of 100 000 steps, followed by 1 000 000 Markov Chain Monte Carlo replicates, assuming an admixture model and correlated allele frequencies.
Each run consisted of a burn-in period of 200 000 steps followed by 10 MCMC (Monte Carlo Markov Chain) replicates assuming admixture model and correlated allele frequencies.
Each run consisted of eight 40 s cycles.
Each run consisted of one cold and three heated chains.
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