@jneen My most recent project was a Python implementation of an aerosol scrubbing model that is implemented as part of larger R code. The R code is not documented to the level we need so the Python implementation was mainly an excuse to chase down references for everything left undocumented or unattributed in the R application. The original documentation was about 50 pages for the entire application; my documentation was about 200 pages for just one model (though strip out the example plots and source code and there's still at least 50 pages of design info and technical basis). In this case the documentation was far more valuable than the code because it cited chapter and verse where every equation, correlation, and piece of data came from. It's good having a pedantic technical reviewer that holds you accountable.

So yeah, I appreciate the work that went into your post and the background detail. :)

Aside: Is a commercial chatbot even capable of providing references for its work? My understanding is that all the attribution is laundered away when the LLM is constructed; all it can produce is obsequious hearsay...