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Errors were reduced by 36.9% (above uncalibrated model performance) when both WRF model data assimilation and hydrological model calibration was utilized.
Variation in the ability of ALGAP to predict species occupancy was likely due to (1) poor model performance when applied to species that choose sites using criteria other than cover type and (2) the inadequacy of ALGAP to describe a heterogeneous urbanized landscape.
This scenario proposes the analysis of the model performance when a bidirectional flow of pedestrians crossing a corridor is simulated.
Table 1 also shows some decrease in the model performance when only connection number point descriptors were used.
In both populations, the lagged decadal oscillations amount to 70% of the model performance when the results from each series are averaged.
First, we assessed model performance when all but one analyte was held constant in the data files, with the value of the chosen analyte randomized.
Similar(36)
There were no substantive changes in the models' performance when we limited the cohort to patients over 40 years-old (Additional File 3).
The model performance decreases when the lead time is extended.
Values tending to unity imply that model performance improved when the variable was replaced (a noise variable).
Model performance increased when stream habitat variables were incorporated, with 12 significant models and an increase in the r values (0.16 0.54).
Model performance improved when chemoresponse was added as a covariate (median recurrence-free survival: 59 months versus not-yet-reached; hazard ratio = 3.91; P = 0.002).
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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