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Looking at the traditional model, we can understand how educating staff about bias can be helpful, but the new model helps us recognize that simply teaching people about bias or offering training isn't enough.
There, alas, the Australian model helps us not at all.
The model helps us visualize the burden of their long days and difficult commutes.
A study of this model helps us present several recommendations for optimizing humidifier performance.
Overall, our model helps us realize good daylighting of a building environment.
Clearly the model helps us to think about the likelihood of a Romney victory: the question is how much.
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History matching of the coreflood model helped us optimize the experiments and was useful in determining the importance of the parameters influencing sweep efficiency in the CWAG process.
This data can then feed into a predictive model, helping us know with precision the actions that are going to accelerate adoption of a new practice, process, or behavior by a given employee group.
Simulation results from this model helped us construct profit probability distribution in a variety of scenarios, including high-tech commodities that have high demand risk as well as functional commodities with lower associated risks.
This hypothesis allows us to develop a simple yet general model, helping us identify the mechanisms that drive the sales of books.
The final analysis focused on to what extent the model helped us to prioritise and systematise our tasks and plans.
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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