diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index 9d6b6ad..a3e9dbf 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -43,7 +43,7 @@ Aggregating gridded data onto polygons is a fundamental aspect of much social an
 
 `xagg` fills a need for an easy, standardized, and accurate workflow for this aggregation. Accepting and outputting data in `xarray` and `*pandas` formats (including keeping by default relevant metadata and attributes from the inputted polygons) means `xagg` can be plugged into a wide array of existing workflows in natural and social sciences, and can easily export aggregated results in formats read by other languages often used in research, including R, QGIS, or STATA. 
 
-Though other `python` packages facilitate the aggregation of raster data, to the authors' knowledge, none provide the same depth of functionality or conduct the final aggregation internally. The `mask_3D_frac_approx` function from the `regionmask` package [@hauser_regionmaskregionmask_2023] also creates weights from relative overlaps between grid cells and regions, for example; this however only works for regular rectangular grids (while `xagg` works with any rectangular grid), and results in more approximate overlaps than those calculated using `xagg`. In addition, none allow easy weighting by a secondary raster variable (e.g., population density or yield), or keep polygon metadata intact. 
+Though other `python` packages facilitate the aggregation of raster data, to the authors' knowledge, none provide the same depth of functionality or conduct the final aggregation internally. The `mask_3D_frac_approx` function from the `regionmask` package [@hauser_regionmaskregionmask_2023] also creates weights from relative overlaps between grid cells and regions, for example; this however only works for regular rectangular grids (while `xagg` works with any rectangular grid), and results in more approximate overlaps than those calculated using `xagg`. In addition, none allow easy weighting by a secondary raster variable (e.g., population density or yield), or keep polygon metadata intact (which is often needed to merge in other datasets after aggregation). 
 
 `xagg` has already been used in peer-reviewed (e.g., @pulla_grace_2023-1; @mastrantonas_forecasting_2022; @schwarzwald_importance_2022) and upcoming (e.g., @sichone_assessment_2024; @peard_combining_2023) scientific publications, has reached over 15,000 cumulative downloads across versions, and is a key component of a how-to guide for climate econometrics [@rising_practical_2024].