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In this context, it is necessary to understand which of the commonly used segmentation methods is best suited to account for such level of detail.
There is controversy about which method – repeated rectal swab cultures, the use of fecal samples, PCR-based detection methods – is best suited and most reliable for the detection of intestinal tract ESBL-E colonization [ 25– 27].
However, due to the lack of large population-based screening evaluations in China, there is no consistent evidence indicating which screening method or combination of methods is best for the country.
There is no consensus on which of these methods is best with the human capital model often noted as overestimating actual productivity loss and the friction cost method being difficult to implement given its requirement of detailed data on labour market conditions [ 28, 29].
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Some debate exists over which assessment methods are best.
First, propensity score methods are best applicable in large data sets.
Due to the weakness of these methods, statistical methods are best suited to our goal of understanding complex road accidents.
We observe that speech-based methods are best at recognizing sadness while achieving similar performances for happy and anger.
There are many possible methods for searching and evaluating feature subsets, but little consensus on which methods are best.
However, model performance can vary significantly and the appropriateness of which methods are best for a given application remains questionable.
Second, which methods are best suited to predicting energy-relevant interaction based on the knowledge of these contextual factors?
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