Sentence examples for levels of prediction from inspiring English sources

A fatigue model based on the ratio of dissipated energy change is independent of the material types and shows relatively high levels of prediction accuracy.

A variety of modelling variables were considered, to ascertain which should be modelled for different applications and required levels of prediction accuracy.

The testing of our covariation detection method on 15 other alignments showed that the accuracy of contact prediction is highly variable depending on which sequence alignment is used, and useful levels of prediction accuracy were obtained with only approximately one-third of alignments.

However, acceptable levels of prediction errors are also determined by the context in which the model will be applied.

Pasiouras and Tanna 2010 used the DA and logit analysis to estimate bank credit and showed that both estimation techniques achieved similar levels of prediction accuracy.

Yet, such high levels of prediction required the inclusion of native range size and/or a binary weediness (weedy elsewhere) score [31], in addition to other traits and factors.

To counteract this, Menzel (1953) developed the coefficient of scalability (CS), "…which measures predictability of the scale relative to the level of prediction afforded by consideration solely of the row and column totals" (p. 351).

Previous findings from the DPT-1 study have shown that C-peptide is a good biomarker in predicting T1D onset in children at risk, with a level of prediction ability similar to that of glucose level (24, 25).

A modelling study recently found that with this level of prediction accuracy, genomic selection could substantially improve selection gain per year47.

Such a discrepancy can be explained by the fact that although both of these programs have a high level of prediction accuracy for prion and non-prion protein determination, the precision of the analysis is below 100%35,36.

This level of prediction requires detecting subtle patterns from massive data sets that are constantly in flux: consumers' purchase histories, product preferences, and schedules; competitors' pricing and inventory; and current and forecasted product demand.