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Real street data were chosen to evaluate the energy consumption.
These methods of generating and averaging 4D data were chosen for a couple of reasons.
The data from Well#A (1046 core and log data) were chosen to provide the training patterns.
The major gas product yields always change in time on stream, therefore, the data were chosen from the 5 h single time point.
For each target, 60% of the bio-active data were chosen as training set and the rest remained as testing set.
The categorical data were chosen as the most significant splitting variables to subdivide the data sets for the C storage in the humus layer (categorical splitting variable: humus type), in living trees/dead wood (categorical splitting variable: forestral growth districts) and in the mineral soil (categorical splitting variable: soil type) into subclasses.
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A total of 41,495 informative SNPs were obtained, and different subsets of SNP data were chose from the 41,495 SNPs for further statistical analyses (see below).
The data are chosen with replacement.
50% of all data was chosen for training, 25% for testing and 25% for validation.
This set of data was chosen because it spans the largest pH range.
60% of data was chosen for training the model and the rest for testing.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com