Sentence examples for modelling such errors from inspiring English sources

Exact(1)

Modelling such errors may have diminished the generalisability and relevance of the findings of this study to surveillance operations overall, as they would need to relate to specific quality control procedures.

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While human listeners are quite tolerant of error in these queries, a music retrieval mechanism must explicitly model such errors in order to perform its task.

However, when cells are known to move in a coordinated way and the transition pattern is modeled, such errors can be corrected by successful trackers, which can return the failed trackers to their correct positions.

It allows external events to be modeled, such as interrupts and bus errors, and assures the RISC-V core is functioning properly.

Since the cube of the bubble departure diameter is used in subcooled flow boiling heat transfer models, such large errors are clearly unacceptable, and underscore the need for more accurate bubble departure diameter models.

Realistically modeling the effects of such errors on sequence data is difficult, but even a simplistic approach may prove instructive in describing their impact on demographic inference.

The effects of such errors in model-based and model-assisted estimation were evaluated by Saarela et al. (2016).

When simulating a full 3D wind turbine or when using CFD models for the calculation of the aerodynamic coefficients for use in 1D BEM models, such an error is not acceptable.

Achieving a high accuracy of replication at the sequence level is clearly an important challenge for early organisms, and other types of models such as error-threshold models [ 18] address this point Relatively accurate sequence replication must have been achieved relatively early in evolution before genomes that contained many genes could evolve.

About 57% of the ELM model's absolute errors were small in magnitude (±0.25), whereas the MARS and M5 Model Tree models generated 53% and 48% of such errors, respectively, indicating the latter models' errors to be distributed in larger magnitude error range.

We investigate several sources of such errors, including model formulation, grid resolution, and solver error for a set of community test problems, which have received attention in the literature as a vehicle to evaluate and evolve optimization methods.

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