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This model predicts accuracy in delayed-recognition tasks on the basis of the similarity coordinates of the stimuli.
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The model predicted accuracy is 97.4%.
The proposed failure mode model predicts with accuracy the failure mechanisms.
The model predicts with accuracy the influence of the methanol feed concentration on the cell performance and the correct trends of the current density and methanol feed concentration, on methanol and water crossover.
The model fits these data well and predicts accuracy close to ceiling, as was observed empirically.
Our model predicts with ∼75% accuracy the bird community composition of any part of the landscape.
The model predicts, with good accuracy, the growth of hydrocarbons and the formation of benzene and aromatic species.
We show that the model predicts with great accuracy the tension compression asymmetry and the evolving anisotropy of the material.
It is demonstrated that the analytical model predicts with good accuracy the shear strength of RC beams strengthened with HCPs.
The proposed model predicts with high accuracy the aging elasticity of cement-based materials, with a minimum intrinsic material properties (same for all cement-based materials), and 11 mix-design specific model parameters that can be easily obtained from the cement and concrete suppliers.
Thus, in humans, our model predicts that imputation accuracy can be improved by generating small population-specific custom reference panels to augment existing collections such as those of the HapMap or 1000 Genomes Projects.
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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