diff --git a/docs/repeated_subsections/reproducible_envs.rst b/docs/repeated_subsections/reproducible_envs.rst
index 56972b7c..762b6f5d 100644
--- a/docs/repeated_subsections/reproducible_envs.rst
+++ b/docs/repeated_subsections/reproducible_envs.rst
@@ -125,7 +125,7 @@ explorer (or by using the File menu) and selecting "New File".
     
 
 Then, you will want to save the file with the extension ``.ipynb`` since we are making
-a jupyter notebook. Then we will want to open the file and select the Python interpreter
+a jupyter notebook. We can now open the file and select the Python interpreter
 that we would like to use:
 
 
@@ -138,7 +138,8 @@ that we would like to use:
 .. image:: ../user/images/vscode_tutorial/selecting_correct_venv.png
   :align: center
 
-And now we are done! We can now use all of the packages in our ``.venv`` virtual environment.
+And now we are done! We can now use all of the packages in our ``.venv`` virtual environment
+inside of our jupyter notebook:
 
 .. image:: ../user/images/vscode_tutorial/show_gerrychain_import.png
   :align: center
@@ -178,6 +179,52 @@ You will now see that the ``.venv`` is available in your list of kernels:
   :align: center
 
 
+.. warning::
+
+  As opposed to VSCode, Jupyter Lab does not automatically use the virtual environment that
+  you have in your project, so you will need to make sure that you have the correct kernel
+  installed before opening the lab. You can see which kernel you are using by looking at the
+  output of the following command:
+
+  .. code:: console
+
+    jupyter kernelspec list
+
+  this will output something like
+
+  .. code:: console
+
+    Available kernels:
+      .venv      /Users/username/.local/share/jupyter/kernels/.venv
+      python3    /usr/local/share/jupyter/kernels/python3
+
+  You will then need to inspect the output of this command to see where the kernel is located.
+  In the above example, we can see that the kernel ``.venv`` is located at 
+  ``/Users/username/.local/share/jupyter/kernels/.venv`` which is the correct location for the
+  current working project. However, if we were to make a new project in 
+  ``/Users/username/Desktop/another_project`` and make a new virtual environment in this
+  location with the same name of ``.venv``, then, after running the command
+  ``python -m ipykernel install --user --name=.venv``,
+  the kernel will still look like it is located at 
+  ``/Users/username/.local/share/jupyter/kernels/.venv``, BUT this new kernel is actually
+  the one for for the second project and not the original! This is important to note because
+  if you are working on multiple projects and you have the same kernel name for each project,
+  things can get a little confusing, so it is best to always reinstall the appropriate kernel
+  before opening the project in Jupyter.
+
+  Of course, an easy fix for this is to just use a different name for the kernel in each project.
+  For example, I might make my kernel name for the project in 
+  ``/Users/username/Desktop/gerrychain_docs``
+  ``venv_gerrychain_docs`` via the command
+
+  .. code:: console
+
+    python -m ipykernel install --user --name=venv_gerrychain_docs
+
+  and the kernel name for the project in 
+  ``/Users/username/Desktop/another_project`` ``venv_another_project``.
+
+
 We can now make a new notebook and select the kernel that we would like to use: 
 
 .. image:: ../user/images/jupyter_tutorial/make_new_file.png
diff --git a/docs/user/optimizers.rst b/docs/user/optimizers.rst
index e36adae7..32bc17ba 100644
--- a/docs/user/optimizers.rst
+++ b/docs/user/optimizers.rst
@@ -137,7 +137,7 @@ And now we can run each of the optimization methods and collect some data!
 
     # Tilted Runs
     min_scores_tilt = np.zeros(total_steps)
-    for i, part in enumerate(optimizer.tilted_run(total_steps, p=0.125, with_progress_bar=true)):
+    for i, part in enumerate(optimizer.tilted_run(total_steps, p=0.125, with_progress_bar=True)):
         min_scores_tilt[i] = optimizer.best_score
 
 We can then plot the results to see how each method performed:
@@ -195,9 +195,7 @@ Likewise, the methods are similar as well:
     scores_sb = np.zeros(total_steps)
     for i, part in enumerate(gingles.short_bursts(10, 1000, with_progress_bar=True)):
         max_scores_sb[i] = gingles.best_score
-        scores_sb[i] = gingles.score(part)    min_scores_sb = np.zeros(total_steps)
-        for i, part in enumerate(gingles.short_bursts(5, 2000, with_progress_bar=True)):
-            min_scores_sb[i] = gingles.best_score
+        scores_sb[i] = gingles.score(part)    
 
     # Simulated Annealing
     max_scores_anneal = np.zeros(total_steps)
diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py
index 8ba2164b..f551e492 100755
--- a/gerrychain/optimization/gingleator.py
+++ b/gerrychain/optimization/gingleator.py
@@ -159,7 +159,8 @@ def reward_next_highest_close(
             (or opportunity) district.
         :type threshold: float
 
-        :returns: Number of opportunity districts + (1 - the next highest district's distance from
+        :returns: Number of opportunity districts +
+            (next highest district - (threshold - 0.1)) * 10
         :rtype: float
         """
         dist_percs = part[minority_perc_col].values()
@@ -188,7 +189,7 @@ def penalize_maximum_over(
             (or opportunity) district.
         :type threshold: float
 
-        :returns: Number of opportunity districts + (1 - the maximum excess)
+        :returns: Number of opportunity districts + (1 - the maximum excess) / (1 - threshold)
         :rtype: float
         """
         dist_percs = part[minority_perc_col].values()