Need advice for testing of hypotheses related to the self-citation method

[oshfdk]

So the VM and the randomly scrambled VM "chunks" behave in the same way? Does this pattern carry over to texts we know have meaning? I know people have used the KJ Bible before to test for "meaningful" text but maybe a VM-contemporary herbarium or scientific text would be better suited. No idea if there are any readily available transcriptions on the internet though.

My go-to lab rat is Opus Majus, and it shows the same pattern. Maybe it has something to do with how the probability of encountering a single edit substring grows with the number of candidates. I don't really want to spend much time on this, I just thought I got an interesting result, but it turned out to be not very interesting. Maybe the metric I have chosen - the distribution of distances to the closest element - is flawed. In any case, I have attached the source above and it begins with a detailed high level and low level description of what the script does (generated by the same ChatGPT), so you can just drop it into an AI bot and ask it to make any needed changes. In most cases when the source has both the source itself and the detailed natural language description, AIs can adapt and debug it in all kinds of ways.

Edit: essentially, the question is there are two indexed sets of random numbers with roughly the same mean and variance, but unknown shape of the distribution. What would be the mean and variance of a set where for each index you pick the minimum of two numbers from the original sets. My original intuition was that the mean would be something like 0.5-0.7 of the original mean, but now I understand that it could be quite different if two sets are not independent. Could be anything.

I also overlooked the fact that there might be no single edit twin for some of the substrings at all. I don't even know what would the script do in this case.