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First, in order to accurately calculate input features used for the ordinal regression, the proposed method uses not only previously observed prediction errors in a target area but also those propagated from its neighboring areas based on atmospheric movements.
In line with these expectations, we observed prediction accuracies, which were slightly smaller than reported previously for maize hybrids (Massman et al. 2012) and similar to those reported for wheat (Zhao et al. 2015) and rice hybrids (Xu et al. 2014; Xu et al. 2016).
Indeed, we observed prediction errors during the task acquisition.
Nonetheless, at the HLA-DQA1 and HLA-DQB1 loci, we observe a few specific alleles dominant the observed prediction errors.
Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation decisions.
It is interesting to note that we also observed "prediction error -type rerror -type thalamus and insula, with incresponsestinathalamuslowing unexpected cues during reversand.
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In order to avoid bias, we repeated 5-fold cross-validation 20 times to observe prediction performance and experimental result was shown in Table 1.
In order to avoid bias, we repeated 5-fold cross-validation procedure 20 times to observe prediction performance on the training dataset RB147 (see Materials and Methods for details).
The great Danish physicist Niels Bohr once observed that "prediction is very difficult, especially if it's about the future".
Good agreement is observed between prediction and measurement for all these characteristics.
It was observed that prediction of cubic EOSs was in close agreement with experimental data with AARD% of less than 1.
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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