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The aim of this paper was to introduce a machine vision based approach as a primarily step for fabricating an automatic wheat purity determination and grading device.
Grading success was not improved by changes in feeding sequence (30.6 ± 10.2%) or altered colour of the grading device (30.9 ± 13.8%).
The grading success (percentage of graded animals/total gradable individuals) with stimuli (light, feeding sequence and colour of the grading device) and without stimuli was analyzed over 9 days.
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The variables histologic grade, device generation (first or second generation), age, menopause status, provider, oral contraceptive use, and histologic tissue type (with five levels) were selected as predictors (although the device generation and provider identification were confounded).
The linear mixed-effects model can be represented as log 10 I p i = β 0 + ∑ m = 1 7 β m x m p i + b z p + ε p i, (3)where β m and x m p i, m = 1,…,7, represent the coefficients and variables of the predictors histologic grade, device generation, age, menopause status, provider, oral contraceptive use, and histologic tissue type for patient p and site i.
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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