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We consider two cases for the data generating probability measure: the model assumption and local deviations from the model assumption.
We consider two cases for the data generating probability measure: the model assumption and local contaminations or deviations from the model assumption.
This violates the model assumption that the distribution of the residual errors is constant.
It is semiparametrically efficient when the model assumption holds, and at the same time it enjoys desirable robust properties when it does not.
Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption.
The model assumption, that repeated freezing and thawing events are necessary for the production of sediment from saprolite (14 cycles of the freeze and thaw with a freezing threshold of − 1 °C), is supported by comparisons of the simulated results and the observations.
The model assumption of the generation process for user's utterance is described as follows.
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The model assumptions were informed based on published clinical trial data and other peer reviewed studies.
Details on the model assumptions and on the considered vector control strategies can be found in the Appendix S1.
Check if the model assumptions are correct and complete.
In the next section we describe the model assumptions.
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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