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The model quality was determined by performing a linear regression between the modeled and experimental potential fields.
The model quality was assessed with MolProbity60.
The model quality was checked with the program PROCHECK56.
Furthermore, enhanced model quality was achieved compared to pure manual segmentation techniques.
Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening.
Model quality was compared across segmentation algorithms using Akaike information criterion (AIC).
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One technique for assessing model quality is fivefold cross validation, which gauges how well the model is expected to generalize to an independent data set.
A new taxonomy of issues related to CAD model quality is presented, which distinguishes between explicit and procedural models.
Individual image processing steps are explained and the magnitudes of their influence on model quality are demonstrated and compared.
Issues of model quality are emphasised, together with model reuse, the development of libraries of sub-models, the potential of generic descriptions and use of modelling within procurement.
When replacing System Experience by SAS scores, model quality is improved (as AICc becomes smaller).
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CEO of Professional Science Editing for Scientists @ prosciediting.com