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A copy of the Grampian cohort dataset was forwarded to the Medical Records Linkage Team at ISD who undertook the linkage.
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During the race condition, the transformed dataset is forwarded to the second stage before the partitioning ranges are calculated by the reducers, causing its mapping operation to be initiated, which would result in an error, as the partitioning ranges are required.
These fuzzy input datasets are forwarded to the next step, the inference engine.
These linked datasets were forwarded to the investigators with identifiers removed.
The unified and transformed datasets are then forwarded to the next stage (stage 2) and to a sampling process, which is performed by a separate flatMap transformation.
Following feature selection, the reduced dataset was modeled with a forward stepwise selection method with the selection p-value of 0.1 and the exit p-value of 0.05.
The key predictors that remained significant in the exploration dataset were taken forward to the replication and confirmation datasets.
This dataset was still massive – but easier to manage.
The 408 dataset was divided into training and validation data (300 training and 108 validations) for single hidden layer in three-layer feed forward back propagation algorithm.
Each dataset was independently processed.
Moreover, the induced signals for the test dataset were generated using an independent forward solver in the frequency-domain, introducing an additional source of discrepancies in forward modelling.
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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