Sentence examples for amount and variability from inspiring English sources

The magnitude and timing of peak flow events are major determinants of the amount and variability of SYs.

Our results clearly demonstrate that the amount and variability of pollen deposition onto plant leaves is considerably higher than previously assumed.

By applying a Generalized Linear Model (GzLM) it was shown that sea transport is the dominant pathway affecting the amount and variability in beach litter loadings.

This relationship was used to estimate the amount and variability in the pollen density on the leaves of maize, Urtica and three other host plants in the range from 0.2 m to the next pollen source (within the field) to 4.42 km away under commercial cultivation conditions.

Using five years of wind data, they then estimated the amount and variability of electricity that the system could generate.

We employ a simple, two-step querying system despite the vast amount and variability of data contained in the mirEX 2.0 database.

This implies that (inasmuch as the variances of NURSING and GP/L could be assumed as sufficient indicators of postnatal treatment) RCF and CT pups were exposed to similar amounts and variability of postnatal care, the only difference between the two types of postnatal treatment being the intrinsic instability of the caregiver.

Some existing climates, such as Mediterranean climates and regions, are historically highly variable in terms of environmental parameters, including the amount, timing and variability of temperature and rainfall.

One of the most drastic human actions is dam construction, altering the amount, timing and variability of freshwater delivered to estuaries.

Unlike past related work, stream continuity is not expressed as the average amount of synchronization loss, but as a combination of the average amount, and the variability of the duration of synchronization loss occurrences.

More specifically, it requires: (1) to determine which classifier algorithms can handle the amount and the variability of data; (2) to evaluate the stability of classifier parameters; (3) to select the best feature set used as input data in order to find the good trade-off between classification accuracy and computational time; and (4) to establish the classifier accuracy over large areas.