diff --git a/.gitignore b/.gitignore
index e274d2c..0f16435 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 # TEMP
-literature/
\ No newline at end of file
+literature/
+backup.tar.gz
\ No newline at end of file
diff --git a/README.md b/README.md
index 5836923..64e3c50 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,15 @@ Final Dissertation Submission Repository
 ## Solution Diagram
 -- todo --
 
+## Classifier Refinement
+[See RAGAS_Service](/supporting/RAGAS_Service/)
+
+## Agent Refinement
+[See agent](/supporting/agent/)
+
+## Generated Database Link and Usage Experiments
+-- todo --
+
 ## Repository Structure
 ```
 ├── run.sh                          # Bash script to run project elements from one place
diff --git a/agent/README.md b/agent/README.md
new file mode 100644
index 0000000..4a77017
--- /dev/null
+++ b/agent/README.md
@@ -0,0 +1,3 @@
+## Refining the agent output
+
+TODO: Table and document experiments
\ No newline at end of file
diff --git a/agent/nodes/verificationSetup.ts b/agent/nodes/verificationSetup.ts
index 700b1d3..86fd0d4 100644
--- a/agent/nodes/verificationSetup.ts
+++ b/agent/nodes/verificationSetup.ts
@@ -15,10 +15,11 @@ export const verificationSetup: GraphNode<typeof MessagesState> = async (state)
 
     for (let i = 0; i < parsed.length; i++) {
       const search = parsed[i].SearchQuery
-      const data = await queryScraper(search);
-      const output = await rankAndDisplayData(data, search);
+      // const data = await queryScraper(search);
+      // const output = await rankAndDisplayData(data, search);
 
-      parsed[i].context = output;
+      // parsed[i].context = output;
+      parsed[i].context = "NONE"
     }
     
     return { proposedTriggerEvent: parsed, proposedTriggerEventIndex: 0 };
diff --git a/supporting/RAGAS_Service/README.md b/supporting/RAGAS_Service/README.md
index 841a0fb..05440b7 100644
--- a/supporting/RAGAS_Service/README.md
+++ b/supporting/RAGAS_Service/README.md
@@ -1,3 +1,15 @@
+# Classifier work for evaluating model quality
+
+Made using a dataset of 1000 labeled claims from MVP pipeline.
+
+Roberta model trained on an augmented dataset with LLM generated adversarial examples for low frequency labels.
+
+Flan model trained using raw labelled claims, inherrent natural language ability allows for pattern recognition without the need for fake data.
+
+Regression model trained using the roberta dataset.
+
+Used ensemble model in the final version, with the component models available on Hugging Face. 
+
 | Model                                                      | % Correct | % Valid taken forward|Used in ensemble|Link
 |------------------------------------------------------------|-----------|----------------------|----------------|-
 | Original                                                   | 53.22     | 61.72                |
diff --git a/supporting/dbkf/fetch.py b/supporting/dbkf/fetch.py
index 99dbfb7..5ca246a 100644
--- a/supporting/dbkf/fetch.py
+++ b/supporting/dbkf/fetch.py
@@ -16,18 +16,18 @@ BASE_URL = "https://dbkf.ontotext.com/rest-api/search/documents"
 
 # "documentTypes": "http://schema.org/Claim",
 DEFAULT_PARAMS = [
-    ("concept", "http://weverify.eu/resource/Concept/Q212"),
+    ("documentTypes", "http://schema.org/Claim"),
     ("from", "2000-01-01"),
     ("to", "2026-02-19"),
     ("lang", "en"),
-    ("limit", 5000),
+    ("limit", 7000),
     ("page", 1),
     ("orderBy", "date"),
+    ("organization", "http://weverify.eu/resource/Organization/128573c5d49d37558706194e755f152d"), # Science Direct
     ("organization", "http://weverify.eu/resource/Organization/3727f7b2aa90ec0716693e5464b28d18"), # StopFake
-    ("organization", "http://weverify.eu/resource/Organization/c71953fa6cf24ac4178f751c77862070"), # CheckYourFact
 ]
 
-NUM_RANDOM_CLAIMS = 40
+NUM_RANDOM_CLAIMS = 200
 
 INPUT_FILE = "../../data/input.jsonl"
 OUTPUT_FILE = "../../data/claims.json"