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Verification And Limits

Quality is the agreement between source lineage, evidence meaning, runtime behavior, publication membership, and public language. Successful software verification can show that encoded contracts hold; it cannot make incomplete evidence complete or convert a contextual layer into direct support.

Quality Model

flowchart TB
    Provenance["provenance integrity"] --> Trust["proportionate public claim"]
    Semantics["evidence semantics"] --> Trust
    Runtime["runtime correctness"] --> Trust
    Publication["publication contracts"] --> Trust
    Language["visible limits and caveats"] --> Trust
Dimension Required evidence
provenance integrity source identity, version or retrieval context, hashes where governed, and transformation lineage
evidence semantics preserved nulls, precision, chronology meaning, locality basis, and ambiguity decisions
runtime correctness focused unit behavior, regression contracts, and end-to-end effects at owned boundaries
publication integrity declared scope, deterministic membership, feature traceability, and exclusion accountability
public honesty wording that stays within the weakest governing evidence and exposes material limits

Validation By Risk

Change Minimum proof direction
scientific normalization or eligibility focused unit tests plus evidence and publication regression contracts
collection behavior source-family tests, staging/replacement behavior, and collection-summary validation
map or report generation scoped publication tests, traceability checks, and reviewed generated diffs
public documentation strict site build, link/navigation contracts, and claim-language checks
packaging or release behavior package-specific build, metadata, smoke, and workflow checks

The exact command depends on the changed owner. Validation should expand with risk, not with habit.

A public claim inherits the narrowest valid posture along its evidence chain. High-quality processing cannot compensate for a missing scientific fact:

flowchart LR
    Capture["captured source"] --> Meaning["interpreted meaning"]
    Meaning --> Relation["typed relationship"]
    Relation --> Admission["product admission"]
    Admission --> Display["visible output"]
    Gap{"material gap?"}
    Capture --> Gap
    Meaning --> Gap
    Relation --> Gap
    Gap -->|yes| Narrow["qualify, exclude, or refuse"]
    Gap -->|no| Claim["publish bounded claim"]

For example, exact map rendering does not make a region-level locality exact, and a resolved archive project does not make an unresolved sample date sample-specific. The downstream representation must preserve the upstream limit or become more conservative.

Claim Posture

A visible output may be:

  • descriptive: it reports governed membership or source-backed fields;
  • contextual: it frames another record without becoming direct evidence;
  • decision support: it ranks or compares under declared inputs and limits;
  • qualified: it remains visible with a material precision or coverage caveat;
  • refused: required evidence is absent or the stronger claim is unsupported.

Refusal is a quality outcome when publication would otherwise overstate the evidence.

Evaluate A Public Result

A trustworthy result lets a reader answer five questions without inferring from presentation alone:

  1. What is counted? Identify the observation unit: sample, site, sequence, grid cell, locality, project, or publication.
  2. Which population is counted? Separate captured, normalized, reviewed, eligible, admitted, and published records.
  3. What role does the layer play? Direct evidence, primary context, contextual domain, sampling context, and geographic framing support different claims.
  4. How precise are place and time? A visible point or date label must not be read more precisely than its provenance permits.
  5. Where are non-members explained? Scope exclusions, unresolved evidence, source gaps, and scientific refusals should remain visible.

The order matters. Starting from a compelling map and reasoning backward can mistake symbol precision for evidence precision. Start with the observation unit and eligible population, then inspect role, time, place, and exclusions.

For example, the Nordic bundle contains 2,172 mapped SEAD sites while its reviewed inventory contains 2,195 rows. The difference is accounted for by 23 rows without country assignment. That is stronger quality evidence than calling either number the unqualified “SEAD total.”

Quality Is Not Uniformity

The source families do not become equally complete or directly comparable by passing through one runtime. Quality means preserving their differences:

Family example Legitimate quality claim Overstatement
AADR release and panel identity are pinned for selected sample rows the country filter is representative of past populations
LandClim or Neotoma pollen context retains source and temporal posture every site measures the same time interval or method
SEAD or RAÄ archaeological context is mapped within declared coverage contextual density directly explains aDNA or pollen observations
animal aDNA admitted points retain sample, locality, chronology, and coordinate lineage all samples expected from every tracked project were recovered
boundaries the product scope is geographically explicit polygons provide scientific evidence about the features they contain

Governing References

Current generated posture remains visible in the animal atlas readiness, animal exclusion report, repository truth posture, and repository claim audit. Those surfaces report current state; they do not override the source and evidence records that govern individual claims.