Sentence examples for benchmark error from inspiring English sources

This paper presents a benchmark error analysis of various approaches for treating multiple domain calculations within an anelastic finite difference model.

The same benchmark shows error rates for the NAST-based methods of above 3.37 and 2.19%.

The following performance metrics were used in the benchmark process: Mean error (GB) Mean error Median error (GB) Median error Mean absolute error (GB) Mean absolute percentage error Mean runtime Median runtime.

Despite a higher likelihood of benchmark-set errors in these regions, this second 'less restrictive' call-set is useful in evaluating the relative sensitivity to variants in known problematic regions (for example, areas of high GC) that are typically excluded from high-confidence call-sets and exome-based target regions.

In Table 2, we observe that for the benchmark model the error is much larger as for the non-holidays.

In addition, by adoption of this approach it has been demonstrated that a highly trained analyst is capable of achieving scores in the repeat analysis of samples that varied by 30%, which is within the accepted benchmark for total error of a typical quantitative biomarker/pharmacodynamic assay such as an ELISA [ 14, 25, 32].

Utilizing the FEA result as the benchmark, the calculating errors of each compliance components of the mechanism were given in Table 1.

The goal of the test suites introduced in this article is to provide a standardized benchmarking environment for error analysis one that enables us, and others, to lay out a quantitative perspective on a set of errors a CR system may produce.

In traditional analysis, neither the deviation of the benchmark of tooth trace error nor that of tooth profile half-angle error is considered, while the deviations exist in engineering.

Open image in new window Fig. 11 Benchmark ACL and relative error of mined ACL in time-section 1 and time-section 7.

The distributed hybrid classification approaches are compared to a benchmark algorithm where the error rates are evaluated in dependence of different WSN parameters.