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Besides, this model obtains an acceptable accuracy and a wide applying scope for engineering calculation.
Compared with other state-of-the-art models, the proposed model obtains an optimal Peak Signal-to-noise ratio (PSNR).
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The model obtains a good fit to the available population data and is fit for risk assessment purposes.
By dividing this value by the cost of MV line per metre, the model obtains a key decision metric, 'MVmax' for each demand node.
This means that the model obtains a zero coupon yield curve but one that is estimated without reference to an input yield curve.
Our classification model obtains a sensitivity (SE) of 89.28%, specificity (SP) of 93.75% and an area under the curve (AUC) of 0.986.
Only when the extinction fraction is low (10%), the pure birth model obtains a slightly higher marginal likelihood than the birth-death.
The results are competitive with those of MMA (the proposed model obtains a better solution for instance S5 when compared to MMA).
The discrimination ability model obtained an AUC (95 % CI) of 0.808 (0.72 to 0.89).
The discrimination ability model obtained an AUC (95%% CI) 0.81 (0.72 to 0.898).
The model obtained an R2p of 0.93 and RPD of 4.09, indicating that the model is adequate for analytical purposes.
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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