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A comparison was first made to evaluate the vocal tract length estimation for each speaker.
Moreover, the accuracy of the exon length estimation for any species will increase if more data are available and if the ascertainment bias (e.g. resulting from overrepresentation of certain gene families in the sample, or from inclusion of only partially sequenced genes) is kept at the minimum.
We expected a similar pattern for our complex simulations; however, model misestimation did not have nearly as much impact on branch length estimation for these datasets.
However, even when parameters are fixed in ML analyses of simple simulated datasets, the lengths of long, deep branches are significantly overestimated, suggesting that error remains in ML branch length estimation for some combinations of branch length, branch depth, and dataset size.
These results suggest that the position of the branch on the tree affects branch length estimation; however, the number of taxa in the dataset does not appear to affect branch length estimation for branches of the same depth on the tree.
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However, the right bit-lengths estimation for fixed-point is a time-consuming task since it is a combinatorial optimization problem of minimizing the accumulative arithmetic computation error.
Although error in the branch-length estimation for the individual gene trees can also contribute to uncertainty in the final date estimates [ 120], it is likely to be less important than the variation present between the different genes themselves.
In this paper, the main characteristics that need to be considered while designing an individual a-priori length estimation technique for today's integrated circuits are discussed.
We compared branch length estimation accuracy for depth 1 and depth 2 branches separately.
After the code length estimation, we search for the minimal polynomials which are the factors of the generator polynomial by the algorithm described in section 4.2.
Branch length estimation was critical for comparative analyses in this study, so the genetic dataset required unambiguous genetic alignments and the maximum gene coverage of the species analysed.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com