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Fig. 3 Performance in the recognition test (excluding the critical contact item) as a function of expertise.
In Figure 3(a) we present the probability that a user collects an item as a function of the number of friends who have collected the item.
The probability of collecting an item as a function of the normalized exposure shown in Figure 6(b) again better distinguishes the original data from their randomized counterparts than the raw exposure shown in Figure 6(a).
When the test items are binary scored (e.g., true-false, or yes-no), ICCs tend to be S-shaped curves and match the probabilities of examinees successfully answering a test item as a function of their ability.
Because of shared proportionality to item degree, if we plot the probability for a user to collect an item as a function of the number of friends that have collected it (f_{ialpha}), we essentially see the effect of preferential attachment.
The influence of goal-directed salience factors was determined by analyzing the letter detection probability in each CVS item as a function of similarity with the target.
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Fig. 3 Accuracy of subject's consistent items as a function of the number of inconsistently recalled items.
Fig. 2 depicts subjective rating results on questionnaire items as a function of visual feedback delay length.
These results further support the hypothesis that the visual system selects potentially relevant items as a function of the demands imposed by the task.
Rasch models characterize the responses of persons to items as a function of person and item measures.
The curve obtained by plotting the precision at every position in the ranked sequence of items as a function of recall is called the precision-recall curve.
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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