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The x-axis shows the number of examples it has and the y-axis its average change in log (hERG) inhibition.
To list a few examples, it has disowned affirmative action, finding no difference between Jim Crow and laws designed to help disadvantaged minorities.
Before applying the method to real data examples, it has been successfully verified on noise-free numerical examples; it has recovered the actual model parameters.
Assuming second-order approximations in some typical examples, it has been found that only two to three exact analyses are needed to achieve the optimum.
Among other examples, it has moved away from using open standards towards proprietary technologies, and it has failed to provide data to developers that it should have if it were truly serious.
Despite these examples, it has been shown, however, that in many countries affected by conflict the government is often not able to provide health services.
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For example, it has not held its own against European deer after its introduction to New Zealand and Europe.
For example, it has a system for the big refrigerators it uses in its stores.
For example, it has been shown earlier that CNP suppresses the expression of its own receptor, NPR2 [ 40].
For example, it has been generalized to multidimensional vector spaces.
But a Trust may have a defence if, for example, it has staff shortages.
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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