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Correcting for that created a model that let scientists to compare apples with apples, which showed the difference between the simulation and observations shrank considerably.
However, the NASA study shows that the previous estimates were indeed biased low, and correcting for that bias brings them into agreement with estimates using other approaches.
If an unknown random effect is the dominant source of uncertainty, it is not appropriate to conduct performance evaluations without correcting for that random effect (increasing the uncertainties, correcting the biases or the both).
Correcting for that location bias lowers our estimates of PAs' forest impacts by roughly half.
Things like attendance, using multiple techniques, differentiating, etc. Do not take grades or test scores into account, especially without correcting for that teachers' student makeup.
But also after correcting for that, RIsearch still shows a significant speedup.
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But it may be possible to correct for that.
So we need to think about how to correct for that.
So starting in 2000, the government phased in a "Birth/Death Model," which tried to correct for that.
What was sold as a model of efficiency and a mirror to reality was crippled by the biases of its creators, and no supercomputer could correct for that.
The bulletin from Boeing described how erroneous data from a sensor could cause an automatic system to push the nose of the plane down and how to correct for that.
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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