diff --git a/docusaurus/docs/protocol/primitives/probabilistic_proofs.md b/docusaurus/docs/protocol/primitives/probabilistic_proofs.md
index 282f2e3db..d68909bdf 100644
--- a/docusaurus/docs/protocol/primitives/probabilistic_proofs.md
+++ b/docusaurus/docs/protocol/primitives/probabilistic_proofs.md
@@ -58,14 +58,19 @@ TODO_IN_THIS_PR: Reference the papers from justin taylor, Alin Tomescu, and axel
 
 Consider the hypothetical scenario below as an extremely rough approximation.
 
-Network state and parameters:
+TODO_IN_THIS_PR: Turn this into a table.
+
+Network parameters:
+
+- Session duration: `1` hour
+
+Network state:
 
 - Median Proof Size: `1,000` bytes
 - Number of services: `10,000`
 - Number of applications: `100,000`
 - Number of suppliers: `100,000`
 - Number of suppliers per session: `10`
-- Session duration: `1` hour
 - Number of Proofs per session: `1`
 
 Conservative (simple) scenario:
@@ -195,7 +200,7 @@ The dishonest Supplier's strategy:
 - Submit false Claims repeatedly, hoping not to be selected for Proof submission.
 - Accept that eventually, they will be caught and penalized.
 
-#### Modelling a Dishonest Supplier's Strategy using a Geometric PDF (Probability Distribution Function)
+#### Modeling a Dishonest Supplier's Strategy using a Geometric PDF (Probability Distribution Function)
 
 The number of successful false Claims before getting caught follows a [Geometric distribution](https://en.wikipedia.org/wiki/Geometric_distribution):
 
@@ -214,7 +219,7 @@ Recall:
 - **Failure**: The network does not catch a dishonest Supplier
 - **Success**: The network catches a dishonest Supplier
 
-#### Modelling a Dishonest Supplier's Strategy using a Geometric CDF (Cumulative Distribution Function)
+#### Modeling a Dishonest Supplier's Strategy using a Geometric CDF (Cumulative Distribution Function)
 
 Above, we have been tracking the probability that `Pr(X=k+1)`, the probability
 of `k` failures (Supplier escapes without penalty) until a single success (Supplier)
@@ -223,6 +228,8 @@ is penalized. This can be modeled using a Geometric PDF (Probability Distributio
 In practice, we need to track the likelihood of `k or less` failures `Pr(X<=k)`,
 until a single success. This can be modeled using a Geometric CDF.
 
+TODO_IN_THIS_PR: Remove the paragraph below.
+
 To simplify the math, we'll be using the Expected Value of a Geometric PDF
 due to its [simpler proof formulation](https://en.wikipedia.org/wiki/Geometric_distribution#Proof_of_expected_value), guaranteeing the results be **AT LEAST**
 as secure when compared to the Geometric CDF.
@@ -421,7 +428,7 @@ You can generate the graph above with `penalty_vs_proof_request_prob.py`
 - **Threshold Value**: Set the `ProofRequirementThreshold` low enough that most Claims are subject to probabilistic Proof requests, but high enough to prevent excessive Proof submissions.
 - **Short-Circuiting**: Claims above the threshold always require Proofs, eliminating the risk of large false Claims slipping through.
 
-##### Modelling `ProofRequirementThreshold`
+##### Modeling `ProofRequirementThreshold`
 
 `ProofRequirementThreshold` should be as small as possible so that most such that
 most Claims for into the probabilistic bucket, while also balancing out penalties
@@ -464,7 +471,7 @@ maximize `Pr(X<=k)` to ensure `k or less` failures (Supplier escapes without pen
 This does not affect the expected reward calculations above, but gives a different
 perspective of what the probabilities of success and failure are.
 
-## Conclusions for Modelling
+## Conclusions for Modeling
 
 By modeling the attack using a geometric distributions and calculating expected values, we can:
 
diff --git a/docusaurus/docs/protocol/primitives/probabilistic_proofs_morse.md b/docusaurus/docs/protocol/primitives/probabilistic_proofs_morse.md
index 20eaf8034..f373da097 100644
--- a/docusaurus/docs/protocol/primitives/probabilistic_proofs_morse.md
+++ b/docusaurus/docs/protocol/primitives/probabilistic_proofs_morse.md
@@ -33,7 +33,7 @@ This is a specification & proposal that will be submitted to [forum.pokt.network
   - [Flow](#flow)
   - [Scaling Benefits](#scaling-benefits)
     - [Block Data Verification](#block-data-verification)
-- [Attack Modelling](#attack-modelling)
+- [Attack Modeling](#attack-modeling)
   - [Approach](#approach)
   - [Definitions](#definitions)
   - [Example](#example)
@@ -136,7 +136,7 @@ This goes to show that reducing the number of Proofs & Claims submitted on-chain
 ![img2](https://user-images.githubusercontent.com/1892194/236548602-bb6cbc2a-aa2a-4b92-ae75-d40eda80685f.png)
 ![img1](https://user-images.githubusercontent.com/1892194/236548608-be569088-a19a-4759-8d7b-2c4f8c5e7ae8.png)
 
-## Attack Modelling
+## Attack Modeling
 
 In order to select the values for the three parameters, the attacker's likelihood of adversarial reward & penalty must be modeled.