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Plots of the significant predictors are provided as Supplemental Figure 1, and parameter estimates for baseline models are provided as Supplemental Table 1.
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For this purpose, a general description of energy system modeling is provided to facilitate the discussion, strict mathematical definitions for baseline and baseline adjustment are given, and the M&V plan development is formulated as an M&V modeling problem.
Several metrics to compare our models with baselines are provided in Tables 1 and 2. In particular, we report the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), the Relative Squared Error (RSE), the Relative Absolute Error RAEE), and (R^{2}).
Baseline characteristics are provided in Table 1.
Baseline data are provided in Table 1.
Both are better for Trump fans than 98percentt, but the parameters of the baseline model are pretty conservative.
Baseline demographic and laboratory data are provided.
Average, systolic and diastolic errors are provided in each plot relative to the baseline model.
Closed-form mathematical relationships are developed, and recommendations are provided for the level of deviation and uncertainty associated to different models, in relation to a baseline model, both for panel surface acceleration and radiated sound power.
Again, before the inclusion of independent meteorological predictors, an AR(1) model fitted from only DPOP (hereafter model 3) is provided as a baseline.
One of the main rationales for building a vaccination component on top of an existing published model [ 23, 24] was that baseline estimates for all the model parameters were provided for a given hospital setting in the US.
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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