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The ODD + D protocol also incorporates a section on 'Theoretical and Empirical Background' to encourage model designs and model assumptions that are more closely related to theory.
RSPs tend to be very complex due to strong physico-chemical interactions, and a number of the model assumptions that are adequate for traditional unit operations may not be suitable in this new context.
This study confirms the ability of TCNs to determine long-term denudation rates in Australia and highlights some important considerations in the model assumptions that may affect the accuracy of limited sampling in large, low-gradient catchments with long storage times.
An a priori analysis of the flamelet model for diffusion flames is conducted to systematically assess model assumptions that are associated with the asymptotic expansion, the omission of higher-order expansion terms, the consideration of preferential diffusion effects, and the one-dimensional flamelet representation.
The presentation of the model was progressively made with increasing degrees of complexity as a consequence of the model assumptions that were being considered.
However, such approaches are not fully satisfactory because they rely on model assumptions that are not necessarily supported by empirical studies.
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Our EM algorithm retains the BayesR model assumption that SNP effects are assumed to be derived from four different normal distributions, but requires much less computing time than BayesR.
This violates the model assumption that the distribution of the residual errors is constant.
This brings out one of the problems with the faithful model assumption that is hidden, so to speak, in the context of linear systems.
The results support our model assumption that stochastic variability is regressive in probability estimation tasks and show the contribution of such variability to subadditivity.
Given our model assumption that the signal strength is equal for all receivers, we can combine the κ max terms derived in the preceding subsections to obtain kappa_{text{max}}=p-1+pcdotkappa_{text{max,time}}, (44).
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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