final edits to blog post

Author: dlebauer

blog/2024-08-26-gsoc-2024-wraps-up.md

@@ -12,11 +12,12 @@ Participating in GSOC over the past eight years has been an opportunity not to e
 
 This year we had four exciting new projects:
 
-1. Pratik Prakhale's project "Adopting Data Schema for Field Management Events" defined a JSON schema and integrated it into the FieldActivity R Shiny App. This allows for more flexible and standardized data storage of field management events, and makes it easier to expand the FieldActivity App as well as build other robust applications that depend on the Schema
+1. Pratik Prakhale's project "Adopting Data Schema for Field Management Events" defined a JSON schema and integrated it into the FieldActivity R Shiny App.
+This JSON schema allows for more flexible and standardized data storage of field management events, and makes it easier to expand the FieldActivity App as well as build other robust applications that depend on the Schema. Pratik posted his final report as a a pull request to the Field Activity repository [PecanProject/fieldactivity#79](https://github.com/PecanProject/fieldactivity/pull/79).
 
-2. Abhinav Pandey "Optimize PEcAn for freestanding use of single packages" helped to make PEcAn projects more standalone and thus easier for use by the broader community. Many PEcAn R packages depend on each other and to a Postgres database. Abhinav's work refactored code to reduced these dependencies.  https://dev.to/devrx/my-gsoc-experience-pecan-project-bi0
+2. Abhinav Pandey's "Optimize PEcAn for freestanding use of single packages" project helped to make PEcAn packages more standalone and thus easier for use by the broader community. Many PEcAn R packages depend on each other and to a Postgres database. Abhinav's work refactored code to reduced these dependencies. Abinav posted about hhis ["My GSOC experience: PEcAn Project"](https://dev.to/devrx/my-gsoc-experience-pecan-project-bi0) on DEV.
 
-3. Sambhav Dixit: "Machine Learning Downscaling of PEcAn Outputs" used Convolutional Neural Networks to enable downscaling of ecosystem model output to high spatial resolution while retaining information contained in ensemble based uncertainty estimates.
+3. Sambhav Dixit: "Machine Learning Downscaling of PEcAn Outputs" used Convolutional Neural Networks to enable downscaling of ecosystem model output to high spatial resolution while retaining information contained in ensemble based uncertainty estimates. His final report ["Linking Earth System Modeling and Machine Learning: My GSoC Journey with PEcAn"](https://medium.com/@indosambhav/revolutionizing-climate-modeling-my-gsoc-journey-with-pecan-3232f6b18da3) is on Medium.
 
 4. Meet Agrawal worked on "PEcAn Code Hardening by Integration Tests", a project that enhanced the reliability of PEcAn's codebase by improving integration tests.