Did I miss it, or do the authors not bother to explain how the original methodology is wrong? Do we just take their word for it?
1) If applied to any landmark, that landmark causes significant cancer deaths.
2) The result is impossible because these people aren't exposed to anything from the nuclear power plants. The average nuclear plant is a remarkably environmentally-friendly thing.
[0] "There is, in fact, a real chance that you simply cannot get a negative result from this method."
I don’t claim to have an explanation of why actual cancer incidence would have this properly (I constructed my example by having an AI chug the linear programming problem given some constraints I made up so I could see what it looked like).
It is, therefore, absolutely not the case that the average correlation to distance from a random point is zero.
See my other comment:
> It is especially telling that no matter what landmark we applied to the methodology, we have yet to get a negative result. There is, in fact, a real chance that you simply cannot get a negative result from this method.
My guess: The code is not shareable. Maybe its method is laughably disprovable?
Being accidentally correct is only barely better than being wrong, and for many uses (such as when the reasoning is extrapolated and used elsewhere) is no better at all.