Riverscapes Consortium

Tool Discrimination

Tool Discrimination

The following concpets are helpful for discriminating model types.

Interface

RC tools are deployed to users thorugh a variety of interfaces:

Most tools have just one deployment interface, some have multiple.

Tool Grade

We classify the grade of our tools according to their growth from innovative research ideas, through to operational tools in development that (with a little love and patience) can be run by someone other than the developer, on through to more broadly deployablle professional tools that are robust and usable by any user in very diffferent settings.

Our RC Techncial Committee ranks a tool’s grade using the following criteria:

Technology
Readiness Level
Tool Status Badge Vetted in
Peer-Reviewed
Literature
Source Code
Documentation
Open Source User
Documentation
Easy User
Interface
Scalability
TR1 -TR2 Concept
TR3 Proof of Concept
to
TR4 Research Grade
to

to
TR5-6 Operational Grade
TR7-8 Professional Grade
TR8-9 Production Grade
to
TR9 Commercial Grade

None or Not Applicable: • Minimal or In Progress: • Functional: • Fully Developed:

NOTE - The RC does not track concepts or proof of concept tools in its listing. Only

Technological Readiness Levels

These ideas are based on the concept of Technological Readiness Level (TRLs), as originally developed by NASA. The TRLs provide a way to discriminate between concepts and products that are in research phases, in development phases, or ready for deployment to broader audiences or makert. TRLs are illustrated below (from twi-global) and formally defined by the European Union:


Why Bother? Why Go Beyond Research-Grade?

If you’ve gotten to the bottom of this page, you presumably scrolled through or read a bunch of detail trying to encourage investment in making tools Riverscapes-Compliant and hopefully profossional, production or commercialized. The reason is simple. If we believe our science is good enough to inform management, inspire the public to conserve and restore riverscapes, then we need to make the tools that represent that science scalable and accessible. If our science is only relevant to other scientists, then we at least should meet a standard of practice of transparency and reproducability.

Put another way, when we invest in scalability, and adhre to a shared set of common goals, bigger things can happen. One such example is, ironically, how Bezos led Amazon to operate. The video below is a recap of a point Philip Bailey made recently: