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All records are subject to error tracing (logical mistakes such as missing mandatory data, CPR-numbers of incorrect content, unknown hospital departments, etc).
Moreover, the daily routines associated with pathology labs (including quality control and patient security) combined with the error tracing mentioned previously, reduce the risk of error and the effect of any such infrequent errors in research work should not be important.
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In this model, the time-delay effect is originated from shape error traces on the surface of the roll.
The experience is that model checking of such a specification yields very long error traces (providing counterexamples) in the order of magnitude of hundreds of states.
As for the intrinsically human errors (tracing error between two specialists: 18.94% ± 05.30%), the average accuracy is within the inter-operator variability.
These values can be obtained from residual bit-error traces collected over operational networks.
We then use bit-error traces collected over different real networks to empirically compare the proposed and existing protocols.
For experimental evaluation, we use a comprehensive corpus of bit-error traces collected over real-life WSN and WiFi networks at different data rates.
Unless this process is automated, a substantial number of errors would be left undetected and propagate to other resources and tools, making it more difficult for researchers to use pathway data, for each user must debug the errors, trace its origin and contact the data providers to fix them.
Analyzing and interpreting such an error trace to localize and debug the actual specification is a tedious work.
Error traces are presented to the users as easily understandable animations, so that no expertise in temporal logic is needed.
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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