Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-07
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.

A Sierpinski gasket computed by hand

Example

Assume the Axiom of Countable Choice. Let D={(0,0),(1,0),(0,1)} and

K={∑j=1∞2−jdj:dj∈D}⊆R2,s=log⁡3log⁡2.

Then 0<Hs(K)<∞ and dim⁡HK=s. No exact critical measure is asserted.

Facts & Assumptions

Given: The objects, conventions, and hypotheses in the statement above.

[F1]

Under the standing Countable Choice hypothesis, a finite Borel measure with outer mass on K positive and small-set diameter bound Crs yields Hs(K)≥μ∗(K)/C. The mass distribution principle

[F2]

Finite positive s-measure identifies dimension s. Hausdorff dimension is the unique critical exponent

[F3]

Geometric series with ratio 1/2 have tails ∑j>n2−j=2−n. For ∣r∣<1, ∑k≥0rk=1/(1−r), and for ∣r∣≥1 the series diverges

Verification

1.1F3

Every length-n word gives a lower-left corner p=∑j≤n2−jdj and a containing closed square Q=p+[0,2−n]2. There are 3n words, giving distinct grid squares because each coordinate prefix has a unique length-n binary code. Their diameters are 2 2−n; hence their s-cost is 3n(2 2−n)s=2s/2. The series converge coordinatewise by geometric tails, and these covers at arbitrarily small scales give Hs(K)≤2s/2.

1.2F3F5

For u∈[0,1) define ej(u)=⌊3ju⌋−3⌊3j−1u⌋∈{0,1,2}, identify these three values with the listed members of D, and put T(u)=∑j≥12−jd(ej(u)). Each coordinate is a limit of Borel step functions, hence Borel measurable. The vector map is Borel since preimages of open rational rectangles are Borel and those rectangles form a countable basis. Define μ(B)=λ1(T−1(B)) for Borel B⊆R2, with preimages taken in [0,1). Disjoint Borel preimages prove countable additivity; thus μ is a Borel probability.

2.1F4step 1.2

Every ternary prefix event is a half-open interval of length 3−n, hence has probability 3−n. Its image lies in the corresponding square. Also T([0,1))⊆K, so every Borel superset of K has μ-measure one and μ∗(K)=1; no measurability claim about an arbitrary image is needed.

3.1step 1.1step 2.1

For nonempty U of diameter r with 2−n≤r<21−n, each coordinate projection lies in an interval of length at most r<2⋅2−n. Such an interval meets at most four closed grid intervals of side 2−n, allowing all boundary contacts. Thus U meets at most sixteen level-n grid squares. Let R be the closed coordinate bounding rectangle of U; its coordinate side lengths are at most r, so it meets at most sixteen squares. Every u with T(u)∈R has its own prefix square meeting R, so μ∗(U)≤μ(R)≤16 3−n≤16rs.

4.1F1F2step 1.1step 2.1step 3.1∎

For a singleton use its coordinate point rectangle at arbitrarily fine levels; at most four squares contain the point, so its mass is at most 4 3−n→0. Empty sets have zero mass. The diameter estimate therefore holds also at zero. Apply mass distribution with constant sixteen and outer mass one to obtain Hs(K)≥1/16. Combined with the finite upper bound, this gives dim⁡HK=s.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

37 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