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In the proposed model, we select the ADS model as the query traceability data approach.
After calibrating and simulating the model, we select those individuals who received Income Assistance benefits for 12 months.
To make sure that the learning process was successful for each model, we select only the targets for which each classifier scored an AUC greater than 0.90.
In the case of the nanocrystal of Fig. 2, for the background model, we select a frame before the nucleation of the particle.
To solve the above model, we select the level of the feature ((x_{kml} = 1)) that yields the greatest (sumnolimits_{i = 1}^{I} {Q_{i} (u_{ikl} - c_{kl} )}), and this gives the optimal value of (c_{kl}).
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Development of the prediction model: We selected predictive descriptors using feature selection algorithms (provided by RapidMiner 5.1.13), which returned 113 descriptors as presumably predictive.
Then, for each model we selected the best one.
For our final model we selected a time lag of 20 months, to be applied to the F10.7a readings.
To evaluate our initial hypothesis that positioning accuracy is maintained while reducing outliers in a reconstructed model, we selected from our dataset a reference model that allows the best automatic alignment to the real world coordinates (Fig. 2).
For parameter set, in our model, we selected with admixture as ancestry model and the allele frequencies were assumed to be correlated, since it is more reasonable to assume common ancestry of such closely related populations.
From the 44 parameters of the model, we selected 25 (see table 1).
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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