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The results show reasonable performance in most cases, thus indicating that our method could provide an alternative way to effectively predict sorbing solute transport in soils.
Despite the relatively high residual difference, the classification accuracy on the arrhythmia database was still 98%, indicating that our method did restore the physiologically important aspects of the signal.
Remarkably, despite the relatively high residual difference, the classification accuracy on the arrhythmia database was still 98%, indicating that our method restored the physiologically important aspects of the signal.
We found that most of the gene sets had statistically significant literature cohesion, indicating that our method correctly identifies functional coherence of gene sets.
The prediction Sn by our method was 84.95%, higher than the predicted results of CAMP, indicating that our method outperformed CAMP.
The same trend in the fifth experiment was obtained (Table 4), indicating that our method works in large genomes with a high marker density.
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We found several such cases indicating that our methods have the potential to outperform and complement human knowledge.
Likelihood values among resulting trees from the multiple runs were all very similar, indicating that our methods produced several equally likely trees.
The difference in likelihood values among resulting trees from the multiple runs was very small, indicating that our methods produced several nearly equally likely trees.
Comparative results indicate that our method is much better than other methods.
Experimental results indicate that our method runs satisfactorily across a wide range of operating system versions.
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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