doc changes

README.md

@@ -1,7 +1,7 @@
 # Projit
 ### Project Integrator for Decoupled Data Science
-
-> Status: **Beta** Functional.
+ 
+> Status: Functional Release - Active Feature Development
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Tests](https://github.com/john-hawkins/projit/actions/workflows/python-package.yml/badge.svg)](https://github.com/john-hawkins/projit/actions/workflows/python-package.yml)

docs/paper/paper.tex

@@ -176,10 +176,12 @@ \section{Introduction}
 and that the core scientific tasks of conceiving,
 designing and running experiments needs to be managed at an ever increasing scale.
 
-Software approaches to managing data, processes and meta-data are typically referred to 
-as e-science platforms. In general they are either build for specific scientific domains
-\cite{Howe2008,} or they are designed for very specific technology stacks (common in a given domain)
-\cite{Subramanian2013}. 
+Software approaches to managing scientific data, processes and meta-data are typically referred to 
+as e-science platforms. In general they are either built as front-ends for specific 
+scientific domains \cite{Howe2008,Pettit:2010} (leveraging known analytical practices in
+the given domain) or they are designed to faciliate interoperability between different
+technology stacks \cite{Subramanian2013}. Machine learning focused frameworks tend to 
+focus on solving problems of model training and deployment for specific technologies\cite{Alberti:2018,MolnerDomenech:2020}, and hence have limited generality.
 
 Platforms for eScience offer a variety of solutions for these problems including  
 tracking the lineage and management of data, referred to as