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Here due to space constraints we are unable to explain the dataset.
An ordinary least squares regression model does not explain the dataset well (R 2 ~.087).
Readers may ask for more details but due to space constraints we are unable to explain the dataset.
Individual apatite dates are broadly uniform over a wide span of apatite [eU], and this pattern can be used to more tightly restrict the spectrum of viable temperature time paths that can explain the dataset.
In Figure 2, The rasters are helpful, as are the averages, but I'd favor something like PCA to help explain the dataset, as it seems like all patterns are represented in their large population.
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"Dataset creation" section explains the dataset creation and is followed in "Opcode pre-filter" section with a description of the filtering method used.
The adjusted R describes how well the model explains the dataset it was estimated on; the higher the R, the better the model explains the dataset.
We also examined a simple point source model to try to explain the same dataset.
Additionally, weighting each model by its importance to explain the original dataset (i.e. model averaging) allows us to obtain a consensus spatial projection (Vicente et al. 2010).
After grouping the codes in three organizing themes (axial coding), all four investigators discussed and refined the coding scheme to explain the entire dataset.
In the following, we explain the three datasets in more detail.
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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