I mostly concur with your more detailed description of things I left out in order to condense my thoughts to a one-pager.
There is one thing though that I’d like to point out:
If we understand “process of film” to include the capture too, we can get into the situation as seen in Inconsistent with Negative Conversion and Roll Analysis On
Looking at the histograms and metadata, I see that all images were well exposed. Nevertheless, conversions show differences that, imo, can only result from different lighting or exposures when the photos were taken. Chemical film’s light vs. density characteristics aren’t as straight as digital sensor’s characteristics. And while scanning effectively hid the differences that might be present in the original negatives, NLP accentuated the differences which, in the end, lead to the thread mentioned above.
Maybe my conclusions are wrong here, but from converting my old 645 negatives, I’ve learnt to get results that aren’t bull’s-eye every time - also because I mostly took each object once and the takes vary in time-of-day and location as well as climate…
Anyways, NLP is a great help for bulk conversions (my use case) and in most cases, further tweaks are made for art/craft reasons. The fun is in the making and NLP manages the (boring) first steps with flying colours. Thanks again for a great solution @nate 