diff --git a/paper/paper.md b/paper/paper.md
index 037189a..2cf512b 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -34,14 +34,14 @@ In this paper, we therefore present OGS5PY, a Python package which provides a Py
 
 # Example
 
-To exemplify the capabilities of OGS5PY, we quickly demonstrate its potential through a coupling of OGS with GSTools [@zenodo_gstools], a Python-based environment for geostatistical applications. In this example, OGS 5 is used to numerically solve the groundwater flow equation under a pumping test scenario. Here a pumping test with pumping well in the center of a cylindrical three-dimensional domain is preformed until steady state is achieved. The groundwater medium is represented through a heterogeneous conductivity field, itself being generated by the random field generator of the the GSTools Python package. The conductivity field was generated as a Gaussian process model, with a Gaussian correlation function and an anisotropy between the vertical directions and the horizontal direction
+To exemplify the capabilities of OGS5PY, we quickly demonstrate its potential through a coupling of OGS with GSTools [@zenodo_gstools], a Python-based environment for geostatistical applications. In this example, OGS 5 is used to numerically solve the groundwater flow equation under a pumping test scenario. Here a pumping test with pumping well in the center of a cylindrical three-dimensional domain is preformed until steady state is achieved. The groundwater medium is represented through a heterogeneous conductivity field, itself being generated by the random field generator of the the GSTools Python package. The full example script can be found on the github page of the OGS5PY project [1] and is not repeated here for the sake of brevity. Instead we focus on on the most salient components. First, the conductivity field is generated as a Gaussian process model, with a Gaussian correlation function and an anisotropy between the vertical directions and the horizontal direction:
 
 ```python
 cov_model = Gaussian(dim=3, var=2, len_scale=10, anis=[1, 0.2])
 srf = SRF(model=cov_model, mean=-9, seed=1000)
 ```
 
-The full model script can be found on the github page of the OGS5PY project [1] and is not repeated here for the sake of brevity. After the generation of the OGS input files, OGS is run by simply writing
+Then, all OGS input files are written. As explained above, this part is omitted here but can be seen in the example script [1]. After the generation of these input files, the files are written and OGS is run:
 
 ```python
 model.write_input()
@@ -50,7 +50,7 @@ success = model.run_model()
 
 ![Cross-section view of the results from the example script of the OGS5PY package. The image shows three-dimensional conductivity field jointly with the resulting head distribution of the pumping test.](plot.png)
 
-The result can be seen in Figure 1, where the heterogeneous conductivity field from the GSTools package and the resulting head distribution of the pumping test are jointly displayed by virtue of a cross-section overlay of both. The depression cone caused by the pumping well can be seen in Figure 1 through the head distribution with a steep decline in the middle. Though overall symmetrical around the pumping well in the middle, the head distribution shows noticeable fluctuations caused by the heterogeneous conductivity field.
+The resulting output file can be visualized with Paraview and is displayed in Figure 1. Here, the heterogeneous conductivity field from the GSTools package and the resulting head distribution of the pumping test are jointly displayed by virtue of a cross-section overlay of both. The depression cone caused by the pumping well can be seen in Figure 1 through the head distribution with a steep decline in the middle. Though overall symmetrical around the pumping well in the middle, the head distribution shows noticeable fluctuations caused by the heterogeneous conductivity field.
 
 [1]: https://github.com/GeoStat-Framework/ogs5py/tree/master/examples/02_pump_test_het_3D.py