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Hence, a separability performance evaluation on these modeling metrics are conducted, where the separability is considered as a metric to measure the discriminative score-gaps (normalized) between a functional motif model and a large collection of random non-functional ones.
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Metrics are everywhere.
Modern metrics are no kinder.
Piazza's own metrics are promising.
The metrics are divided into three "objectives".
All metrics are open to manipulation.
more, and the metrics are often questionable".
"The quantitative metrics are pretty fierce".
Not that other metrics are any brighter.
Metrics aren't everything: It's particularly difficult to assess progress with metrics.
Honestly, what metrics are available?
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