A database checkpoint is not a production evidence platform
Table presence showed that future provenance could be represented; it did not prove migration replay, leakage control, access policy, or end-to-end lineage.
What the checkpoint established
A research PostgreSQL instance demonstrated that separate namespaces could represent shared biological context, design packages, evidence records, model metadata, and dataset-snapshot metadata. A small number of metadata rows confirmed that the schema was queryable at that moment.
That is useful implementation evidence. It is also narrow evidence.
The checkpoint did not freeze the deployed DDL as ordered migrations. It did not demonstrate backup and restore, role grants, state isolation, prediction-time cutoff enforcement, or a complete lineage traversal. A separate local metadata store also existed and had not been reconciled with the PostgreSQL design.
Representable is not enforced
A field for an observation time can support leakage control, but field presence does not prevent a future value from entering a historical feature set. That requires a negative test that rejects information unavailable at the requested cutoff.
Likewise, separate namespaces can express the difference between a computational candidate and an experimental observation. They do not automatically enforce the promotion policy between those states.
The honest milestone
The correct description was “research database checkpoint.” Before the system can support stronger claims, it needs versioned migrations, public or synthetic fixtures, content checksums, access and restoration tests, privacy-safe projections, and an independent replay of the released lineage graph.
This distinction prevents infrastructure nouns from becoming evidence claims. A
table named model_registry does not prove that a trained model exists. A row named
dataset_snapshot does not prove that a training split was frozen. The artifact and
its tested behavior remain the authority.