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Gene models from the previous run were used for re-training in the following run.
The resulting regression models from the previous three analyses (oocyte/ovum production per polyp, coral cover, and the number of polyps per cm2) were used to estimate the number of ova produced within a hypothetical 1 km2 reef area with increasing depth from 10 50 m (equation 2, Fig. 6).
A few key features have been added to both models from the previous Shine.
This consumes the structure and workload models from the previous stage.
The adaptation can be repeated using the models from the previous adaptation iteration for segmentation.
Step 4 displays the predictions made by the selected models from the previous step along with an image of the predicted structure.
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Therefore, the model from the previous sections is also used here.
In what follows, we estimate two extended versions of our model from the previous section.
Similar to Fondell et al. (2008), we retained only the AIC 'best-ranked' model from the previous step.
A CRBM is a non-linear probabilistic model used to represent time series data consisting of three factors: (i) an undirected model between binary latent variables and the current visible variables, (ii) a directed model from the previous visible variables to the current visible variables, and (iii) a directed model from the previous visible variables to the latent variables.
It then extrapolates to potential impacts this policy could have on the environment at large by forecasting a future production path based on the model from the previous section.
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