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We investigate the relations between model performance and catchment characteristics and find that the climatic characteristic of a catchment is the most important factor impacting model performance.
Our task is to find the set of reservoir parameters that minimizes the difference between model performance and historical performance of the field.
This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision.
In rate parameter optimizations found in the published literature, rate constants of single elementary reactions are usually systematically optimized to achieve good agreement between model performance and experimental measurements.
As a result, a tradeoff was made between model performance and high spatial resolution output maps in actual simulations.
A correlation between model performance and both the number of datapoints and chemical diversity was established (see main text).
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Monte Carlo simulation revealed a strong coherence between model performance in isotope simulation and runoff prediction, demonstrating that the use of isotopes as dynamic proxies of calibration targets helps reliably constrain model parameters.
We distinguish between ideal model performance and pragmatic model performance, where the former refers to the model's performance in an ideal clinical setting where all individuals have fully observed predictors and the latter refers to the model's performance in a real-world clinical setting where some individuals have missing predictors.
Model optimism was then calculated as the difference between model performance in the bootstrap sample and the original dataset, and the final AUC value was set.
However, a compromise should be made between model performance, provision of spatial information details and computing time.
In the implementation of the optimization algorithm, the following objective function is proposed to be minimized for the verification of match between core flood reservoir model performance and predicted data to obtain two-phase relative permeability function.
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between model development and
between model analysis and
between model bias and
between model parsimony and
between model theory and
between model accuracy and
between model output and
between model fit and
between model outcome and
between model complexity and
between model structure and
between model prediction and
between model simulation and
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com