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This model detects saliency based on color and depth contrast and the pop-out effect.
This model detects the degree of focus via monocular focus and the comfort zone. 3.
This model detects the degree of focus via monocular focus and the comfort zone.
This model detects sensitivity differences in experiments used to evaluate chemical-induced transformation.
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It should be noted that in this model, detecting changes in Ag abundance is not based on any kind of active tracking but rather on probabilistic issues.
This is because, when the model detects an overall discrepancy between state and national polls, it adjusts both of them in opposite directions to bring them closer to one another.
Regarding recessions, the model detects most of the recessive periods.
Additionally, the model detects localized skin wrinkling or crushing.
If the model detected no interactions, then all subjects were pooled.
They demonstrated with a data set for a workshop at the fourth Pangborn Sensory Science Symposium that this model generally detects more significant differences between products than ANOVA does.
The above model detects each user anomaly.
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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