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Prior to analysis, for extraction and pre-concentration of target compounds from the aqueous samples, techniques such as liquid-liquid extraction (LLE) and solid-phase extraction have been utilized [16, 17].
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Random sampling techniques, such as over-sampling the minority class or under-sampling the majority class, have little effect on improving the boundary.
In particular, simple sampling techniques such as random undersampling are generally the most effective.
We combined non-probabilistic sampling techniques, such as convenience and snowball methods to draw out our survey participants.
Improvements in k-space sampling techniques such as the use of parallel imaging techniques allow for increased spatial resolution without reduction of temporal resolution.
Spatial sampling techniques such as spatial simulated annealing using prior information [33] can be employed in place of our random sampling approach and compared to our heuristics.
Development of 3D CEST acquisition methods has also been reported using carefully designed data sampling techniques such as 3D-GRASE [39] and 3D-SPIRAL [40].
In some cases, it could be the sampling techniques such as small split spoon cores which would not "see" a mastodon or an archeological site.
For instance, chain-referral sampling techniques such as 'snowball' sampling [3] solicit members of the hidden population to provide contact information on behalf of their peers.
Established repeated re-sampling techniques such as "jackknifing" and "bootstrapping" are used to estimate variance and confidence intervals.[25] In most applications, the re-sampling is based on observations, or individuals within the sample.
AnimDens may also be useful for investigating other sampling techniques, such as other diver techniques, visual surveys, video censuses, or possibly other sampling procedures entirely (e.g. exploring the likelihood of capturing an animal in a mark-recapture study).
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