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The reason for these differences in approximation quality is that it is very difficult to decide which reactions and complexes can be neglected and which cannot.
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A novel aspect of our study was the investigation of whether differences in stable scholastic mathematical ability were associated with differences in approximation-related brain activity.
The results of this study also suggest that different neural mechanisms may be involved in approximation per se and in individual differences in mathematical ability.
These code comparisons have enabled us to understand differences in numerical methods, physical approximations, microphysical parameters, etc.
Previous studies present contradictory results concerning the association between expression divergence and sequence differences in coding regions (as an approximation to divergence time): positive correlations were detected in model organisms such as yeast [ 34, 36] and human [ 52], but not in Arabidopsis thaliana[ 53].
Consistent approximations throughout the analyses as opposed to possible differences in results from different codes owing to differences in gridding structures, spatial and temporal discretisation approximations, and material property assignment schemes; and, 3.
The mean volumetric water contents explained up to 88% of active IRT values, which were a good approximation for relative differences in the spatial and temporal distribution of moisture contents.
This is thus the first study to date to examine individual differences in brain processing of non-symbolic approximation using longitudinal data to select two stable extreme (rather than poor vs. average) groups of children of the same age.
However, the sample sizes using this approximation result in differences in sample sizes compared with exact methods, particularly for relatively small studies.
As a result, samples are normalized for differences in sequencing depth, whereas providing an approximation to the number of reads assigned to each taxonomic group.
By using the estimated costs of chronic pain in the EU5 countries to anchor the calculations, the model (to a first approximation) adjusted for differences in the costs of health care delivery across countries.
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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