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We used the score for PC1 (performed on the five morphological variables described above) as our estimate of size for the froglets.
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Forward step-wise Discriminant Function Analysis (DFA; F to enter = 1.00) was conducted on the following morphological variables (transformed as described above): Bill volume, tarsus length, percent chestnut in crown, percent black in back, and flank color slope.
Thirty-four morphological variables were recorded as described in Table 1.
These cyclic patterns can be identified and characterized by analyzing the distribution of variation in morphological attributes within neighborhoods defined by windows of appropriate dimensions and shape, as morphological variables computed for any given cell describe only a small portion of the total cyclic variation that characterizes a repeating landform type.
To describe the morphological dynamics of vitrified/warmed blastocysts and to identify quantitative morphological variables related to implantation.
The role of morphological variables has also been studied adding to the chemical data set by the morphological data.
This result confirms the importance of the morphological variables for soot discrimination.
Results showed that plot vigour modelling based on these three morphological variables may be relevant.
In fact PC2 and PC1 separate morphological parameters from chemical ones and they contribute to separate the morphological variables in the two different clusters highlighted before.
The model predicts covariation among morphological variables, such as tooth size, intercusp distances, and cusp size.
Principal component analysis (PCoA) of growth and morphological variables.
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CEO of Professional Science Editing for Scientists @ prosciediting.com