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The configurations in this batch are shown in Table 6.
The configurations in this batch are shown in Table 4. Batch 4 examines the performance of the automatic word alignment for each of the partitions when all available resources are used.
The configurations in this batch are shown in Table 2. Batch 2 examines the performance of the automatic word alignment when generalising resources from one partition to other partitions.
The configurations in this batch are shown in Table 5. Batch 5 examines the importance of standard resources, parts-of-speech blocker and MeSH-dictionary for the performance when statistical resources and training resources are used.
The configurations in this batch are shown in Table 3. Batch 3 examines whether the performance of the automatic word alignment improves monotonously when resources from more and more partitions are used or if some partitions worsen the performance when they are included.
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A histogram of the number of iterations until convergence for the first batch is shown in Figure14a.
The distribution of number of lungs from each herd on a given date as well as the number of pigs in the batch is shown in Figure 1.
The results for the low and high SS batches are shown in Fig. 2, and compared to those for the batch with 5%% of liquid seed medium.
The mean SC (± SD) for the different breeds and batches are shown in Table 1.
The drug content, swelling ratio, porosity, and density of factorial batches are shown in Table 5.
The distributions of the raw CNs of the other PRT4 plates and the relative locus dose of the other MLPA batches are shown in Additional file 2 and Additional file 3, respectively.
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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