Sentence examples for sample to explicitly from inspiring English sources

NICE is the only institution in the sample to explicitly differentiate between commercial-in-confidence and academic-in-confidence data, assuming that the latter can be discussed during appraisal meetings despite the fact that they need to not be widely publicly disclosed in order not to prejudice the publication of information in scientific form [ 40].

Finally, we recorded exploration during the two sample trials, to explicitly evaluate the possibility that this could have differentially biased exploration of the objects during the preexposure phase.

We propose a statistical method to estimate PU richness that, unlike phenomenological methods like Chao2, can exploit replication in our sampling design to explicitly model PU occupancy and detection probability.

Mammographic density data are not available in the CIMBA sample to test this hypothesis explicitly.

It has been suggested that the failure of existing MCMC algorithms to explicitly sample zero-length branches could produce unreliable results when the true tree is a star phylogeny [30], but BI remained biased when modified to sample unresolved trees (Fig. S5b,c).

Both of these schemes use an MCMC algorithm to explicitly sample both the demographic and evolutionary model parameters and the structured genealogy: a phylogenetic tree annotated with individual migration events.

In a small sample situation we have to explicitly declare step 2 as not sufficient, as an extended model with an increased number of parameters might be seen as "overkill", however necessary to reveal characteristics of the questionnaire.

Because flowering tree density can have a high variance, both within and among species, our sampling design enables us to explicitly control for this.

There are clearly areas of the map which are sampled more intensively than other, and an inability to explicitly define GLAS sample properties is an important barrier to use of the sensor in global biomass inventories.

There are projects under way to explicitly capture sample and experiment descriptions in unified ways.

It is possible to (1) describe how pseudo-replication in sampling influences the overall variability in response data using mixed-effects modeling, (2) integrate data to explicitly model the sampling process and account for bias using a hierarchical modeling framework, and (3) examine the relative influence of many different or related explanatory factors using machine learning tools.