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The interpretation of the data is fair and balanced.
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The agreement between the predictions of the new model and the experimental data is fair to excellent.
Verizon's resale of its customers' location data shows us just how far companies will go to commercialize their users and that even extremely dangerous and sensitive data is fair game to be resold without users having the faintest idea what's being done with their extremely personal information.
Model fit to data was fair to good, as evidenced by a comparative fit index of 0.975, and the root mean square error of approximation = 0.058.
Despite the fact that we believe our presentation of the data was quite fair and included a lengthy discussion of possible biases affecting our results, several comments by Farmer et al. necessitate a response.
I have tried at all times to scrutinise all data objectively, and I believe the data used is fair, and all of it is available in the public domain.
To achieve a fair comparison, the data were also preprocessed using Ender's 2D calibration algorithm [23] with 1024 receiving pulses before SMI processing.
To facilitate a fair comparison, the data were constructed so that there are at least 10 records per class.
We considered the quality of the data to be fair.
In a Korean study that targeted patients who underwent surgery for the treatment of gastric cancer, the kappa value of CCI comorbidities between the data resources was fair for five diseases; however, excluding these comorbidities, the kappa value was less than 0.2.
Uncertainties should be reduced as far as is practicable during the process of compiling a NFI, and it is particularly important to ensure that any model used and the data collected are fair representations of the real forest status.
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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