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Through analysis of parameter sensitivity, we verified that model predictions were robust to parameter variation.
Robustness testing results showed that the accuracy of predictions for the analyte found in lower concentrations (caffeine) was influenced by relative humidity, while paracetamol/ibuprofen predictions were robust to all factors.
Sensitivity analyses indicated that QBM predictions were robust to variations of ± 10% in parameter estimates.
For most parameters, model predictions were robust to changes in parameter values (Fig. 7).
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Keeping this in mind, we come full circle in understanding why the mDCM flexibility/rigidity predictions are robust despite a greater variance in predicted melting temperatures and other thermodynamic quantities.
The count and presence/absence models were highly correlated within and between the EEZ and DFZ (Pearson's r > 0.80, p < 0.05), suggesting these predictions are robust.
These predictions are robust to a wide range of parameter values.
Evaluations on the seven-year Shenzhen Growth Enterprise Market (China) transaction data show that the proposed system can make effective predictions, is robust to the market volatility, and outperforms some existing methods in terms of accuracy and return per trade.
Further validation of the models should be undertaken to ensure that predictions are robust for the range of site conditions under which Douglas-fir is grown in New Zealand.
Figure 3 shows that CYNTENATOR predictions are robust to parameter choices over a wide range of different parameter settings.
McVean and Charlesworth [59] showed that equilibrium MCU predictions are robust to polymorphism, but this result has not been confirmed for non-stationary evolution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com