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By comparing the predicted and observed maps we were able to examine how well our predictions fitted the observed mortality and in which regions there were mismatches.
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The P-Mod predictions fit the waveforms at DART 21413, 21419, and 21401 quite well.
As Figure 3 shows, Kandlikar's CHF predictions fit the bare SiO2 and Teflon surfaces well, but underestimated the CHF for the fabricated nano/microstructured surfaces, irrespective of the heating contact angle.
Overall, the model predictions fit the observed data.
Model predictions fit the experimental data well, within the 95%% confidence interval.
The model predictions fit the experimental data and all the parameter values have been estimated and provided in [ 39].
That prediction fits the observed frequencies.
The theoretical prediction fits the experimental data with average regression coefficient of r2~0.96.
Finally, the model's prediction fits the experimental results very well by verifying the proposed model with the extensive database.
As can be seen, the prediction fits the data rather well, which supports the conclusion that the bulk of the sequence variants in the dataset are of technical rather than biological origin.
None of the BIRNLex and 5.3% of the FMA predictions fit this case.
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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