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If the description the gold on the table denotes the set having for elements everything that is gold on the table, then how can we evaluate the truth of the sentence?
The cycle column in the table denotes the number of clock cycles needed to compute one data block, except for the FIR filter design.
The matrix element in the table denotes an item of category that was identified by the SVM as belonging to category ; in other words, the rows of this contingency table correspond to the ground truth and the columns to predictions.
The nonparametric statistical comparison (z-scores) in the table denotes the degree of difference between the binding potential values provided by the methods; *the binding potentials obtained by the two methods differed significantly (two-tailed tests, Bonferroni correction for multiple comparisons, p ≤ 0.0016).
Each entry of the table denotes a distinct condition of a glucose unit.
The second last column in the table denotes the total number of UP and DOWN DEGs for different GSE platforms.
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The nonparametric Rho scores in the table denote the correlation between the binding potential values provided by the methods; *statistically significant correlations (two-tailed tests, Bonferroni correction for multiple comparisons, p ≤ 0.0016).
The values in the table denote number of patients and per cent within each risk category.
astatistically significant difference between the groups (p < 0.05, chi-square test) Values in the table denote percent Values in the table denote percent astatistically significant difference between the groups (p < 0.05, chi-square test) The values in table denote Spearman two-tailed correlation coefficient.
Additionally, we also include implicit reasoning support in the table, denoted by the italic letters C and S. That is, we know that credulous reasoning yields the same answer for any AF and argument w.r.t.
The third column of the table denote with the letter "E" the expected reactions, and with a letter "U" those unexpected and incorrect, and with the letter "P" those reactions that are unexpected but that can be easily explained and are plausible in terms of correlation.
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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