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Models at different levels of abstraction are required because each abstraction level possesses its own unique advantages and disadvantages; for example, high-level models enable faster simulation but less accuracy, while lower level models have greater accuracy, but suffer from slow simulation speeds due to the large amount of captured detail.
GFM models have been developed based on the understanding that hybrid models have greater forecasting potentials than single evaluation models (Li and Li 2015).
More complex models have greater representation power (low bias) but overfit to the particular training set (high variance).
Further research is necessary to look at whether more complex models have greater predictive power, and whether the analytic approach is robust at different time and space aggregations.
Further, it is well documented that linear models have greater power to detect marginal effects than interactions (Lewontin, 1974; Wahlsten, 1990).
Figure 4(B) shows how the κ statistic varies with choice of threshold, and indicates that both models have greater skill at intermediate probabilities, but that the zonal model has greater skill over a wider range of probability thresholds.
Similar(52)
Whole-cell models have great potential to transform bioscience, bioengineering, and medicine.
These models have great software and hardware requirements, therefore, difficult to use in industrial practice.
Moreover, our economic models have great difficulties incorporating these major geophysical changes and their impacts in a reliable manner.
These types of models have great potential in a range of geofluid, and other science and engineering, simulations of complex fluid flow.
Compared with the traditional high-throughput computational and experimental trial-and-error approach, the machine-learning chemisorption models have great potential in accelerating the discovery of interesting catalytic materials.
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