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Scalar residuals between the testing dataset and the seven main field candidate models for epoch 2005.0.
Fig. 3. Scalar residuals between the testing dataset and the seven main field candidate models for epoch 2010.0.
Fig. 2. Histograms of the scalar residuals between the testing dataset and the seven main field candidate models for epoch 2005.0.
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The high accuracy of 94.59%, although there are only five cases differing from the lowest accuracy of 81.08%, may be due to high correlation between the test dataset and the third training dataset.
The testing dataset does not intersect with the training dataset.
In our study, among the 226 GO_BP terms that fulfilled our query criteria (size between 50 and 500), 118 had a preservation score of Zsummary (GO_BP) >5 and were considered well preserved between the training and the testing datasets.
In general, a value of Zsummary <2 indicates no evidence of preservation between the training and the testing datasets, 2 < Zsummary <10 implies weak to moderate evidence, and Zsummary >10 indicates strong evidence.
These results, although biased toward models relying on datasets close to the testing datasets (B, F), usefully complement the results of intercomparisons between models.
The left plot in Figure 3 represents the relationships between the individuals in the test dataset and the individuals that are most frequently selected in the training set by the first two principal components of the marker dataset.
Null hypothesis 1: the EFS does not differ between favorable and unfavorable patients from the test dataset.
Table 1 shows the correlation coefficients between the peak scores and their predicted values by PeakRegressor in the test dataset.
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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