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The messy map is a challenge for political analysts, who are finding predictions are much harder than they used to be.
This paper focuses on the climb phase, because predictions are much less accurate in this phase than in the cruising phase.
Compared with the approximate growth models that have been reported, e.g., the invariant field model, the SFFK model and the model by Chen et al., the proposed model's predictions are much closer to the direct detailed approach, especially for growth under high supersaturation conditions.
These predictions are much smaller than experimental observations.
We estimated that the FDRs of our predictions are much less than 21% (for KEGG) and 30% (for GO) because of the percentage of correct predictions for annotated proteins and the incompleteness of GO and KEGG annotations.
Such worse-than-random predictions are much less compact than real proteins.
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The LLSM predictions were much more accurate than the SLSM.
The ANN predictions were much better than those (±24.2%) obtained by the second-order polynomial equation from the RSM.
When more than one parameter value needed to be estimated, predictions were much poorer, mainly due to the large uncertainty associated with the parameter value estimates.
It shows that while the original numerical estimation has low R and high RMSE and MAE (more so for the V- component) the revised predictions were much satisfactory up to 5 days in future.
However, the uncertainty about the predictions was much better quantified by the non-stationary variance model, as indicated by the mean and median of the standardized squared prediction error and by accuracy plots.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com