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However, we expect an error of up to ±12% to be typical for most enterprise web applications when modelling database performance on published IOPS.
Following these quality assessment interventions, we expect an error of approximately 11% in the information considered relevant to the analysis.
However, from a stochastic description of the round-off error one would expect an error growth proportional to the square root in the number of time steps.
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If spherical shape is employed to retrieve the dust model parameters from the observational parameters calculated for non-spherical shape, we expect a systematic error of v eff as large as 40% of its a priori value.
If a monomodal gamma distribution of coarse particles is employed in finding model parameters from observational ones but the true size distribution is bimodal containing 1% fine mode particles by volume, we expect a systematic error of ρ A as large as 20% of its a priori value.
Given these values one can expect a ∼4% error in the stress estimation from experiment to experiment due to variation in beam thickness.
Do authors expect a sharp error-catastrophe-like phase transition *in the absence* of horizontal gene transfer?
You can talk to your professor about it, but do not expect a change unless an error was made.
When the heads of two mice are in close proximity, one would expect an increase in type-2 errors (i.e., assigning the vocalization to the incorrect or non-vocalizing mouse).
In this case, only the resonances of order 2 matter since we are expecting a larger error than in the previous case.
In any functional genomics strategy, one expects a significant error rate because of the high-throughput data collection.
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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