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=12jdj:djD}R2,s=log3log2.

Then 0<Hs(K)< and dimHK=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>n2j=2n. For r<1, k0rk=1/(1r), and for r1 the series diverges

Verification

1.1

Every length-n word gives a lower-left corner p=jn2jdj and a containing closed square Q=p+[0,2n]2. There are 3n words, giving distinct grid squares because each coordinate prefix has a unique length-n binary code. Their diameters are 22n; hence their s-cost is 3n(22n)s=2s/2. The series converge coordinatewise by geometric tails, and these covers at arbitrarily small scales give Hs(K)2s/2.

F3
1.2

For u[0,1) define ej(u)=3ju33j1u{0,1,2}, identify these three values with the listed members of D, and put T(u)=j12jd(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(T1(B)) for Borel BR2, with preimages taken in [0,1). Disjoint Borel preimages prove countable additivity; thus μ is a Borel probability.

F3F5
2.1

Every ternary prefix event is a half-open interval of length 3n, hence has probability 3n. 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.

F4step 1.2
3.1

For nonempty U of diameter r with 2nr<21n, each coordinate projection lies in an interval of length at most r<22n. Such an interval meets at most four closed grid intervals of side 2n, 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)163n16rs.

step 1.1step 2.1
4.1

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 43n0. 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 dimHK=s.

F1F2step 1.1step 2.1step 3.1

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

35 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