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For the other outcome (O2), a user judgement is needed to decide whether any of the variables retrieved would be the appropriate candidate to be renamed (e.g. because it is consistent with other datasets and its values are in the range of what would be expected) or all candidate variables will be dropped.
The proposed classifier is applied to some synthetic and machine learning datasets, and its results are compared with those reported in the previous studies.
Our experimental results, obtained with a prototype running on a representative hardware platform, demonstrate the scalability of the approach on large datasets and its superiority compared to state of the art methods.
Three crucial issues are considered: (1) the incompleteness or discontinuity of the long-term datasets and its effects on the statistical analysis; (2) the consistency of the data transfer procedure from a reference site to a target site based on numerical simulations of the wind fields; and, (3) the techniques to transfer and interpolate data from concurrent anemometric datasets to a target site.
Finally, BCM can handle unbalanced datasets and its applicability is not restricted to datasets with approximately equal cases and controls.
Finally, the biomass growth rate is excluded from the datasets and its prediction used to exemplify the capability of the model to calculate non-measured fluxes.
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The Urban Regeneration and the Environment Research Programme (URGENT) required a system for cataloguing its datasets and enabling its scientific community to discover what data were available to it.
Our analyses suggest that there is no clear a priori feature of a dataset and its representation that would suggest that it is not different from random.
(The open nature of the Korean and Chinese-Douban dataset and its potential for re-use makes it possible for independent observers and readers to replicate and build upon the results discussed below).
Furthermore, it seems obvious that the dataset and its accompanying microarray could be helpful in finding out whether transcriptional regulation is an important driver of adaptive evolution in this species.
Then, the new dataset and its transpose are multiplied to create the modified matrix.
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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