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The carbon and sulfur models were fair and oxygen model was poor.
The information contained in the first model was poor, whereas the second model had a higher predictive value.
Further, the fit of data to the intraparticle diffusion model was poor (R 2 ≤ 0.11).
The quality of the model was poor in vegetable samples classification according to location (see Additional file 2).
The calibration of the original SAPS II model was poor because it strongly over-predicted mortality.
The fit of the final model was poor as judged by r = 0.03 0.04.
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Although the numerical performance of the model is poor, the results it picks seem intuitively pretty good.
However, the predictive capabilities of the model are poor, especially for apparently chaotic behavior.
The Bayesian two-stage approach to design experiments for the general linear model when initial knowledge of the model is poor, is reviewed and extended.
In Studies 1 and 2, results of confirmatory factor analyses showed that the fit indices for the 21-dimension model were poor and that seven items displayed low factor loadings.
Classical optimal experiment design methods have not been widely adopted in practice for biological systems, in part because the resulting designs can be very brittle if the nominal parameter estimates for the model are poor, and in part because of computational constraints.
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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