diff --git a/pages/RecSys_2025_Reproducibility_711.md b/pages/RecSys_2025_Reproducibility_711.md
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--- a/pages/RecSys_2025_Reproducibility_711.md
+++ b/pages/RecSys_2025_Reproducibility_711.md
@@ -18,4 +18,4 @@ collapsed:: true
 		- Technical Soundness: The [[Paper]] defines interesting research questions especially that concerning the inference phase. The methodological pipeline is clearly described in the online material, The insights of the first research questions, while valid, are obvious and expected to me (i[[.]]e., newer hardware being more efficient). It is necessary to stress what's the take away message and why it is [[IMPORTANT]], and especially what's can be built on top of it.
 		- Reproducibility of methods: The online appendix contains all the artifacts that are needed to reproduce the experiments. I've played a bit with the docker image made available by the authors. Even though I could not execute the whole process until the end, I managed to easly start the pipelines by following the instructions made available by the authors.
 		- Impact: Carbon footprint in RS is indeed a relevant and interesting topic. While some results are insightful (e[[.]]g., training/inference trafe-offs) other ones are rather expected and may not be impactfull for the community. However, the emphasis on model validation overhead adds useful considerations for future research direction related to sustainable RS.
-		- Artifacts (Availability and Documentation): An online repository has been made available at
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+		- Artifacts (Availability and Documentation): An online repository has been made available at https://anonymous.4open.science/r/RecSys-2025-Reproducibility-Investigating-Carbon-Footprint-of-Recommender-Systems it includes both the artifacts and the instructions to replicate the experiments. A docker image is also avilable at
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