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When applied to synthetic data, the model performs substantially better than existing methods that consider each network snapshot in isolation.
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A 1-factor model performed substantially worse than a 3-factor model (TRd = 25.172, df = 3, p = .000), indicating that "clarity and support", "enthusiastic teaching", and "application orientation", represent conceptually and empirically distinct constructs.
The context++ model performed substantially better than the most predictive published model, which was TargetScan6.All.
For the PM10 model, cross-validation (R = 0.62) showed the model performed substantially better than other approaches (e.g., inverse distance weighting cross-validation R = 0.29) (Yanosky et al. 2008).
Model-2 performed substantially better; the R2 being 0.92.
Both models performed well.
This molecular-based model, based in a specific subtype of BBD, performs substantially better than traditional models containing clinical and histologic features only; in the validation set, the TTC10 signature provided an improvement in AUC of 0.07 above the BBD-BC model alone and 0.23 above BCRAT alone.
Overall, we found that the strict clock model performs worst, and the t values obtained under the clock vary substantially between sub-schemes.
These results show that our system performs substantially better than [12] in all kinds.
The results indicate that the proposed policy performs substantially better than the baseline approach.
We observe that algorithm NNFDAR always performs substantially better than MSTSAR, which performs conspicuously better than algorithm MTDAR.
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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