E Evidence Press

Press release · 11 August 2026 · version 0.3.3-candidate

What a Finite Expected Spectrum Can Identify

A finite expected site-frequency spectrum observes only finitely many exponential projections; this corrected candidate proves why sharp calendar-window targets can remain unidentified and supplies an exact positive collision.

Listen to this briefingNarrated summary · OpenAI API synthetic voice (fable) · MP3 · download
▶
Watch this briefingVideo summary · YouTube · watch · play on this page

Plain-English summary

A site-frequency spectrum is a compact histogram of genetic variation. Even if its expected entries were known perfectly, it would reveal only finitely many exponential averages of population history. A fitted demographic curve can contain much more detail than those averages, but that extra detail comes from the chosen model or regularisation—not from the finite expected spectrum alone.

This corrected release asks a target-specific question: can the spectrum determine mean population size inside a fixed calendar-time window? Under the paper's neutral one-population coalescent model and declared broad positive history class, it cannot. Calendar time must be recomputed separately for every candidate history; a fixed interval in transformed coalescent time is not a fixed historical interval.

The paper supplies two strictly positive histories with exactly the same finite expected spectrum. For the same dimensionless illustrative calendar windows, their target ratios are approximately 0.441 and 1.794. One represents a depression relative to the reference window; the other represents an expansion. The expected spectrum alone therefore cannot license either directional narrative.

The exact result

Under the stated neutral, panmictic Kingman and infinite-sites assumptions, the expected unfolded SFS for a finite sample is an invertible linear transform of finitely many coalescence moments. Those moments are Laplace coordinates of the transformed population-size history at the lineage rates.

The central theorem considers a fixed calendar window, maps its endpoints back through each history's inverse coalescent-time transformation, and asks whether the corresponding mean—or a ratio of two distinct window means—is constant on every expected-SFS fibre. Over an admissible class containing an L-infinity neighbourhood of a positive constant history, with fixed scale and a common known tail, the answer is no for every finite sample size.

At the constant history, the derivative of a sharp calendar-window mean has a step-function weight. The expected SFS observes only a finite span of continuous exponential kernels. Projecting that step weight away from the observed span produces a target-changing, observation-null direction. A separate composition lemma controls the history-dependent inverse map, including windows that begin at calendar time zero.

This is an exact expected-summary result. It does not say that every restricted parametric demographic family is unidentifiable, or that two histories with the same expected SFS have the same linked-sequence law.

An explicit opposite-target collision

For three sampled haplotypes, the package uses

\[ g(\tau)=4e^{-\tau}-15e^{-2\tau}+12e^{-3\tau}, \]

whose Laplace transform vanishes at both relevant lineage rates. The histories

\[ h_\pm(\tau)=1\pm\frac45g(\tau) \]

are strictly positive and have exactly the same expected unfolded SFS—and therefore the same folded SFS. Yet their ratios for claim window [0.3, 0.6] and reference window [0, 0.1] are 0.4411819836 and 1.7935760644.

The construction uses dimensionless illustrative windows. It is not a rescaling of the proposed 813–930 ka human bottleneck and is not an estimate of human population history.

What folding retains

Conventional folding discards the ancestral-versus-derived orientation of each variant. In the exact unnormalised expected spectrum, the paper proves that folding retains precisely the even coalescence moments. For essentially bounded weights on the declared finite horizon, the identified linear functionals are therefore exactly the span of the corresponding even exponential kernels.

An exact five-haplotype direction changes the unfolded expected spectrum while leaving the folded expected spectrum unchanged. This is a structural identification statement, not a claim that finite data can estimate every retained coordinate stably.

Exact fibres and first-order information are different

The paper also develops a first-order information calculation for a declared fixed-covariance Gaussian tangent experiment. It shows that known linear compression cannot increase regular target information. This local quadratic result is useful for designing augmentations, but it is not promoted to an exact nonlinear identification theorem.

Joint spectra, linkage-aware summaries, ancient DNA, fossils or other external measurements may restore a contrast destroyed by a marginal SFS. Whether they do so is target- and model-specific. The current package contains no reconstructible joint-SFS numerical example and makes no numerical joint- information claim.

Priority correction

The formula-first priority audit materially narrows the originality claim. Myers, Fefferman and Patterson (2008) already distinguish calendar and genetic time, construct positive calendar-time histories with the same allelic spectrum, describe invisible bottlenecks and expansions, and interpret the observation as projections onto exponential kernels. Bhaskar and Song (2014) provide the finite Laplace coordinates, the bridge to the expected SFS, scale equivalence, restricted-family identifiability and folded parity.

The release therefore does not claim to discover demographic non-identifiability, the calendar/genetic-time mechanism, exponential projection structure or finite-Laplace observation. Its defensible object is a target-specific sharp-window formalisation, a bounded proof with explicit function-class conditions, an exact opposite-target witness, an integrated folded-functional statement and a fail-closed assurance package.

The audit is targeted and non-systematic. It does not establish absolute priority for that assembled formulation.

How it was checked

The tagged package passes:

  • 120 deterministic tests under ordinary Python;
  • exception-based replay under optimized Python;
  • exact calendar-collision and folded-rank receipt checks;
  • 11 hostile mutation or negative controls plus one clean comparison;
  • two same-producer expected-SFS forward routes;
  • a 72-file SHA-256 inventory;
  • fresh-extraction replay from the immutable commit; and
  • two consecutive byte-identical PDF builds, followed by PDF structural and every-page visual inspection.

The PDF was rebuilt with readable tables and publication-scale figures. The scholarly creator is Anonymous. Research-direction, repository and publication roles remain available in machine-readable provenance. The active release metadata contains no personal-channel video.

These checks establish bounded producer-side internal consistency, replay and artifact identity. They do not establish theorem truth, unaffiliated reproduction, independent reimplementation, formal verification, specialist acceptance, editorial peer review, bibliographic priority or scientific impact.

What the result does not establish

The result does not establish equality of complete linked-sequence laws, history-dependent count covariances or higher moments. It does not reanalyse human genomic data, validate an empirical error model, reconstruct a joint-SFS example or adjudicate the proposed ancient human bottleneck.

Restricted demographic families may be identifiable when their assumptions are accepted and the sample size is sufficient. This theorem instead concerns a broad positive class and asks which target is licensed by the finite exact expected summary itself.

The most valuable next work

The highest-priority next steps are an unaffiliated clean-archive rerun, a genuinely independent implementation, verified population-genetics and functional-analysis reviews, proof-assistant formalisation and a systematic citation-graph priority audit.

Empirically, a useful study would prospectively freeze one primary calendar- time target, use common processed data, estimate genomic-block covariance and predeclare which linked or external augmentation is expected to eliminate which target-changing direction. A severe-event narrative should survive only if its conclusion remains stable over the resulting shared compatibility set.

Additive correction history

The v0.2.0 and v0.2.1 GitHub tags, DOI records and historical correction evidence remain immutable. Version 0.3.3 is an additive successor. It replaces the active page's old theorem-and-audit synthesis with the evidence actually shipped in the current package, corrects the priority boundary, applies Anonymous scholarly authorship, repairs the PDF presentation and removes the personal-channel video links from current release metadata.

Media

The audio briefing is provided in the header above. Download the MP3 briefing · read the transcript.

The Mathematical Collision of Genetic History · Watch on YouTube

Verification status

Unrefereed candidate. The tagged package passes 120 tests, ordinary and optimized replay, exact collision and folded-rank checks, 11 hostile controls plus one clean comparison, semantic receipt checks, a 72-file manifest, fresh-extraction replay and byte-identical PDF rebuild. The formula-first priority audit is bounded and non-systematic; it found direct collisions for several essential ingredients and therefore forbids first-discovery language. No unaffiliated rerun, independent reimplementation, proof-assistant verification, verified specialist review or editorial peer review is recorded. The theorem concerns an exact expected one-locus summary and does not establish equality of complete linked-sequence laws, history-dependent covariances or higher moments. It does not adjudicate the proposed ancient human bottleneck.

Cite

Anonymous. (2026). What a Finite Expected Spectrum Can Identify (Version 0.3.3-candidate) [Unrefereed candidate]. Zenodo. https://doi.org/10.5281/zenodo.22029532
BibTeX
@misc{sfsidentifiabilityaudit2026,
  title        = {What a Finite Expected Spectrum Can Identify},
  author       = {Anonymous},
  year         = {2026},
  doi          = {10.5281/zenodo.22029532},
  url          = {https://doi.org/10.5281/zenodo.22029532},
  version      = {0.3.3-candidate},
  howpublished = {Zenodo},
  note         = {Unrefereed; internally replayed evidence package. Press page: https://evidencepress.org/releases/sfs-identifiability-audit/}
}

Also: cite.bib · paper.json · this page as Markdown