At least when solving physics problems, you'll frequently scale the equations so the actual values in the problem are around -1..1. Thus having extra precision around -1..1 when solving such problems would be beneficial.
For example, if you're trying to solve the motion of a mass on a spring that gets an initial kick, rather than representing the spring length in meters, you divide by some characteristic length[1] and get a non-dimensional length of order 1. If you want the actual length, you just multiply the non-dimensional length by the characteristic length.
[1]: https://hplgit.github.io/scaling-book/doc/pub/book/html/._sc...
It is indeed, and one of the first things you learn in scientific computing is to make your values fit mostly within [-1, 1].
Floating point numbers have the same precision (relative precision) everywhere.
So between [-1, 1] you can represent a number like 0.1234567 and then 0.1234568 for a min delta of 0.000001.
But around a billion, you can only represent 1,234,567,000 and then 1,234,568,000 for a min delta of 1000.
These are not exactly right numbers, just rough estimates to get the idea, but the point is if you are trying to do something like add 1 centimeter to a position that is 10,000 kilometers from an origin, you're adding 0, no matter how many times you do it, it will never increment.
Thus, since you know a lot of problems will have values around order 1 it makes sense to design the encoding such that you get extra absolute precision for order 1 numbers.