Sentence examples for apply the forest from inspiring English sources

We apply the forest definition reported by the Colombian Designated National Authority (DNA) to the UNFCCC CDM Executive Board: Minimum forest area: 1 hectare Tree crown cover value: 30% Tree height (or in situ potential to reach it): 5 meters.

We have investigated the following questions by applying the forest growth simulation model FORMIND to the situation of the Atlantic Forest in the state of São Paulo, SE Brazil: (1) which set of parameters describing the local regeneration and level of density regulation can reproduce the biomass distribution and stem density of an old growth forest in a reserve?

We applied the forest model to another spruce forest (Tharandt) for the years 1999 2004 (Fig. 3b).

To estimate how much more forest would have been cleared in the protected areas if the lands were not protected, we applied the forest area loss rates detected in the corresponding unprotected areas within the same state.

The EAGB of wet and rain forest lifezones was estimated by applying the wet forest equation: EAGB = ρ × exp - 1.239 + 1.980 × ln DBH + 0.207 × ln DBH 2 - 0.0281 × ln DBH 3 (1)Correspondingly, the EAGB of forests within the tropical moist lifezones was estimated using the moist forest equation: EAGB = ρ × exp - 1.499 + 2.148 × ln DBH + 0.207 × ln DBH 2 - 0.0281 × ln DBH 3 (2).

As shown in Fig. 1b, we applied the random forest classifier to held out patients from Stanford to flag potential FH cases, and evaluated the predictions with manual chart review.

A diagnostic model was obtained by applying the random forest (RF) method to the training set [54].

Applying the random forest algorithm, we performed a regression of the mean fold change of 1,277 mRNAs as a function of 355 interacting miRs.

Identifying combinations of miRs that putatively are associated to altered mRNAs expression levels we applied the random forest algorithm [40], an ensemble-learning-algorithm that constructs regression trees with bootstrap data samples and random choices of predicting variables.

To the best of our knowledge this study is the first one applying the random forest algorithm for immune cell differentiation.

As it has previously been shown that, when an ensemble model is employed, building the model from a fused set of data types is favored compared to fusing ensemble models built from the individual data type [ 35], we combined the two data types by fusing them into one feature set before applying the random forest algorithm.