Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-13
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

The Gauss map preserves Gauss measure

Example

Assume countable choice. On X=[0,1) put G(0)=0 and G(x)={1/x} for x>0. The Borel probability μ with density h(x)=1/[log(2)(1+x)] relative to Lebesgue measure is G-invariant. The same map preserves its completion. This example proves measure preservation only.

Facts & Assumptions

[F1]

A nonnegative measurable density defines a measure by integration over sets. The indefinite integral of a nonnegative measurable function is a measure.

[F2]

The logarithm has derivative 1/x on the positive reals. The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t.

[F3]

The logarithm is continuous, strictly increasing, vanishes at one, and obeys the quotient law. Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm.

[F4]

Continuous functions on closed bounded intervals are bounded and Riemann integrable. A continuous function on [a,b] is Riemann integrable, by Heine-Cantor and Riemann's criterion.

[F5]

The Riemann integral of an integrable derivative is the primitive difference, including one-sided endpoint derivatives. The second fundamental theorem: if G is differentiable on [a,b] with G=f and f is integrable, then abf=G(b)G(a).

[F6]

Under countable choice, bounded Riemann integrable functions have the same Lebesgue integral. A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral.

[F7]

At most countable sets are Lebesgue measurable and null under countable choice. Every at most countable subset of Rn is Lebesgue null; in particular λ1(Q)=0.

[F8]

It suffices to check a measurable self-map on a generating pi-system for a finite measure. Measure preservation can be checked on a generating pi-system.

[F9]

A measure-preserving transformation extends to the completed measure space. Compositions, iterates and completions preserve invariance.

[F11]

The rationals are countable. Q is countably infinite.

Verification

Given: Assume countable choice. On X=[0,1) put G(0)=0 and G(x)={1/x} for x>0. The Borel probability μ with density h(x)=1/[log(2)(1+x)] relative to Lebesgue measure is G-invariant. The same map preserves its completion. This example proves measure preservation only.

1.1

By [F3], log2>log1=0, so h is positive, bounded by 1/log2, and continuous on [0,1]. By [F4] it is Riemann integrable on every closed subinterval. The derivative of log(1+x) is 1/(1+x) by [F2], using the translated difference quotient, also one-sided at subinterval endpoints. Thus [F5] and [F6] yield [a,b]hdλ=[log(1+b)log(1+a)]/log2 for 0a<b1. Singletons and countable sets have zero density integral because they are null by [F7] and h is bounded. Consequently endpoints do not change this interval value. By [F1] the density defines a Borel measure, and the value with a=0,b=1 is one; removing the endpoint 1 does not change it.

F1F2F3F4F5F6F7
1.2

The Borel sets Jn=(1/(n+1),1/n]X, n1, partition (0,1). On Jn, G(x)=1/xn, and on the singleton {0} it is zero. Each branch is the restriction of a continuous real function and takes values in [0,1), so for every open subset of X its inverse image is a countable union of Borel branch inverse images and possibly {0}. This proves Borel measurability. For 0<s<1, the exact inverse image is G1[0,s]={0}n1([1/(n+s),1/n]X). The displayed intervals are pairwise disjoint because 1/(n+s)>1/(n+1). The only endpoint outside X is 1 when n=1.

1.1
2.1

Using the interval integral of step 1.1 and countable additivity, μ(G1[0,s]) is 1/log2 times the sum over n1 of log(1+1/n)log(1+1/(n+s)). The quotient law [F3] and the identity (1+1/n)/(1+1/(n+s))=(1+s/n)/(1+s/(n+1)) rewrite the partial sum through M as log(1+s)log(1+s/(M+1)). All original summands are nonnegative. Continuity of log at 1 gives the limit log(1+s), so μ(G1[0,s])=log(1+s)/log2=μ([0,s]). For s=0, the preimage is {0}{1/n:n2}, an explicitly enumerated countable null set, and both masses are zero. The full space X also has equal inverse-image mass one.

1.11.2F3F7
3.1

The family consisting of X, the empty set, and all [0,s] with 0s<1 is a pi-system. It generates the Borel sets of X: complements give (s,1), and increasing unions of initial closed intervals give [0,b); intersections give ordinary open intervals, which form a countable rational-endpoint base for the interval topology. Conversely all generators are Borel. The measure is finite and G is measurable, so [F8] applies to step 2.1 and proves preservation for every Borel set. By [F9] the map is measurable and preserving on the completion as well. Countable choice is inherited in the Lebesgue/Riemann comparison, null-set and completion suppliers; the branch sums and partial-sum telescoping make no choices. The rational-base assertion uses [F10]. The rational-base assertion uses [F11].

1.11.22.1F6F7F8F9F10F11
4.1

The corresponding inverse-branch density calculation can also be seen directly. On 0<y<1, the inverse branch is ηn(y)=1/(n+y), with ηn(y)=1/(n+y)2. Hence h(ηn(y))ηn(y)=1/[log2(n+y)(n+y+1)]. The algebraic identity 1/[(n+y)(n+y+1)]=1/(n+y)1/(n+y+1) gives the partial sum [1/(1+y)1/(M+1+y)]/log2, tending to h(y). This verifies the density balance numerically; the interval proof in steps 1.1–3.1 already establishes measure preservation without assuming a change-of-variables theorem. The separate endpoint computations in step 2.1 account for y=0.

1.11.22.13.1

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

99 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources