The phrase "a more robust source" is correct and usable in written English.
It can be used when discussing the reliability or strength of information or data sources in various contexts, such as research, analysis, or decision-making.
Example: "For our project, we need to find a more robust source of data to support our findings."
Alternatives: "a stronger source" or "a more reliable source".
Compared to methods that require some training (semi-supervised or supervised), our approach provides a more robust source-to-distortion ratio (as we will show in Section 3) because the separation process does not depend on a supervised training.
The relative advantage to public health of analyzing telephone triage data as a separate, potentially more timely data stream vs. combining these data with ED or ambulatory care data for a larger, possibly more robust source remains to be determined.
The variation among different GCMs indicated a more robust data source to be used for future carbon modeling.
In other words, a more robust pacemaker source not only guarantees a much higher safety factor for the spread of excitation but also leads to a shorter AP conduction delay and a better entrainment of repolarization as well.
Therefore, these inputs may not be applicable to all hospitals and may vary should more robust sources be identified.
In this respect, the lack of relevant study-level covariates in the reported articles precluded a more robust assessment of sources of this heterogeneity.
On the other hand, using a wider window for the mean allows to obtain a more robust estimate of the source shape in transients where harmonic sinusoidal parameters are less reliable than in voiced segments.
Admittedly, one limitation of this method lies in the number of available studies with detailed covariates information, which prevents a more robust assessment of heterogeneity sources [ 67].
Using estimated pitch values f ̂ 0, the spectral comb filter is build to suppress the influence of the already estimated speech sources in the CPSD; this leads to a more robust estimation of the remaining speech sources.
In order to effectively interpret the landscape as represented through LiDAR, it is critical to supplement this data with multiple contextual sources and a more robust theoretical geographic framework.
