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We find that the success of breakthrough predictions is highly sensitive to the definition of the rock-physics transform; it is therefore important to model this transfer function accurately.
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For the subset of cells for which the model could be cross-validated on responses to novel stimuli (n = 23), the predictions were highly accurate, with correlation coefficients between predicted and actual responses of 0.62±0.09 for the V stimulus and 0.66±0.07 for the VN stimulus (for firing rate in 2 ms bins).
Although it is very likely that our predictions are highly reliable we separated the evidenced from the predicted datasets within the front-end and now provide two sub-versions of CoryneRegNet.
In the comparison, we found remarkable similarities between concentrations predicted by the large-area LUR and the dispersion model; the model predictions were highly correlated and showed very similar levels.
Generally, the Biological Process predictions were highly nonspecific for most models.
It is shown that the FE predictions are highly consistent with the experimental study [1].
The model predictions were highly sensitive to mass-loading of nitrogen from OWS and the denitrification rate coefficient.
The predictions are highly satisfactory and the ANNs have been found to be reliable processing systems for modelling and simulation in the traffic data assessments.
Matthew Whittaker, chief economist at the Resolution Foundation, said: "If the OBR has a better sense of where revenues are coming from, then we should go with that; but it does highlight the fact that all these predictions are highly uncertain, and there's an element of risk in taking that money and spending it".
Sensitivity analysis showed that DO, CBODf, TN, and TC predictions are highly sensitive toward headwater flow and point source flow and quality.
Although the predictions were highly dependent on the assigned steady velocities and the long-term relaxation time constants, they indicate that the coseismic subsidence of the Yamoto station in Miyagi prefecture will recover around the year 2020.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com