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Distance trees were created using the heuristic distance search for optimal trees by PAUP and the maximum likelihood method by PHYML.
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For the main phylogenetic analysis, corrected distance was performed in PAUP*v4 using the heuristic search with tree-bisection-and-reconnection branch swapping.
Since the NN-tanglegram heuristic performs similarly on trees when using the splits-based distance H or the shortest path distance matrix H′, we will present the results only for the former variant.
Most of these methods, however, use heuristic distance functions that are not based on any explicit model of molecular evolution.
The expected number of breakpoints was set to 1 (default setting) and several 2-point reductions (Base TSP+MLE, Extended TSP+MLE, 2-point LOD distance) [ 27] were solved using the LKH heuristic methods [ 28].
MP bootstrap percentages were obtained after 1,000 replications using the same heuristic search strategy using Paup*.
UPGMA is a heuristic greedy method that creates one cluster per network and sequentially merges the nearest pair of clusters by directly using the distance matrix until only two clusters remain.
In the following, we calculate some of the distances randomly and some using the similarity heuristics which we describe next.
When only performing disambiguation of the toponyms annotated in the gold standard corpus, it had a precision of 0.88 P. The metadata heuristic proposed in this article led to a 26% increase in precision over using the standard population or distance heuristics.
We used the two heuristics, Population and Distance heuristics, separately to compare their respective precisions.
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