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We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs.
Figures 11 and 12 present the precision and recall results when the numbers of total retrieved images are 10, 20, 30, and 40, respectively.
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We present the precision-recall curves for MAXIF, PRINCE and RWRH in Figure 3B.
This paper presents the precision enhancement of five-axis machine tools according to differential motion matrix, including geometric error modeling, identification and compensation.
Thus, corrective changes that would enable the synoptic method to generate more reliable approximations are proposed, and a new optimised methodology is presented; the precision of the new method relies on synoptic observations but is independent of weather conditions.
Figure 9 presents the precision and success plots per frame.
Figure 2 presents the precision of k-NN query for different methods when k was varied from 1 to 35.
Figure 2 presents the precision, recall and F-measure of yeast protein complex prediction by PINALOG-A.
In Table 2, we first present the average precision, recall, F scores and Area under the Curve when LinearSVM, DecisionTree and RandomForests were used to build the classification models.
According to Ryd, a 3D precision is mathematically difficult to present since the precision is different for each direction (x, y, and z) and cannot easily be added.
As far as we know, their work presents the highest precision in protein fold recognition published thus far.
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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