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This template determines the pair of colors with the larger contrast ratio: color1/color2 or color1/color3. This is useful for selecting a foreground/background color pair. For accessibility, WCAG 2.0 AA guidelines require a contrast ratio of 3 or larger for large text, and 4.5 or larger for normal sized text.
This template checks for compliance with WCAG G17, i.e. that a background–foreground colour combination has got a contrast ratio of more than 7:1.It takes two arguments, the base colour value, and the tracking category, category, to place non-compliant transclusions in.
No one color model is necessarily "better" than another. Typically, the choice of a color model is dictated by external factors, such as a graphics tool or the need to specify colors according to the CSS2 or CSS3 standard. The following discussion only describes how the models function, centered on the concepts of hue, shade, tint, and tone.
A contrast is defined as the sum of each group mean multiplied by a coefficient for each group (i.e., a signed number, c j). [10] In equation form, = ¯ + ¯ + + ¯ ¯, where L is the weighted sum of group means, the c j coefficients represent the assigned weights of the means (these must sum to 0 for orthogonal contrasts), and ¯ j represents the group means. [8]
For templates without strong features, or for when the bulk of a template image constitutes the matching image as a whole, a template-based approach may be effective. Since template-based matching may require sampling of a large number of data points, it is often desirable to reduce the number of sampling points by reducing the resolution of ...
The maximum is taken over all possible structure superpositions of the model and template (or some sample thereof). When comparing two protein structures that have the same residue order, L common {\displaystyle L_{\text{common}}} reads from the C-alpha order number of the structure files (i.e., Column 23-26 in Protein Data Bank (file format) ).
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A contrast set learner will produce a set of association rules that, when applied, will indicate the key predictors of each failed tests versus the successful ones (the temperature was too high, the wind pressure was too high, etc.). Contrast set learning is a form of association rule learning. [2]
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