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Such methods scale as N7, where N measures the system size, limiting their application to fairly small species for routine use.
We also provide detailed scalability studies for both the above-mentioned problems which show that our methods scale extremely well up to several hundreds of processors.
As for other facets of the interpretive process (including parsing), use of deep domain knowledge for metonym processing can be quite effective in sufficiently narrow domains, while corpus-based, shallow methods scale better to broader domains, but are apt to reach a performance plateau falling well short of human standards.
The new approach also helps to ensure that any lessons learned from the assessment are captured and used to inform evidence-based interventions for similar problems through the use of a bespoke evaluation framework (adaptation of the Maryland scientific methods scale, see Sherman et al. 1998) and the implementation of an intranet based library.
Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks.
The OTU-picking methods scale well on large datasets, but the results are highly sensitive to the similarity threshold.
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The modelling approach is based on the integration of ecological and socio-economic assessment methods, scale-specific and GIS-based data and knowledge modelling and visualization techniques.
It is shown that the cost of both methods scales as O(NsNtlog2Nt).
If additional studies are undertaken and it is found that scale-up-based methods routinely produce higher estimates than existing methods, scale-up-based methods may not be appropriate for estimating the sizes of populations most at risk of HIV/AIDS.
observations from the network at each time point and study how the performance of different methods scales with the number of i.i.d.i.d
Standard deviations of each histogram were different, probably due to differences in the detection methods, scaling, etc. used by the two groups.
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