News monitoring of the Sustainable Development Goals (SDGs) can be distorted by a missing archive interval, a broad lexical rule, or duplicated evidence. We study how to audit such conclusions when target-domain relevance labels are unavailable. Our case comprises 16,060,006 URL-unique candidates retrieved from English, Spanish, Russian, and Chinese GDELT Article List (GAL) titles observed in 2024–2025. We replace whole-day gap accounting with a minute-interval ledger, correct public-label processing to distinguish accepted, rejected, and unassessed text–goal pairs, and compare weak signals on source-grouped public benchmarks. We find that weak fusion does not satisfy our predefined retention rule. We therefore use a claim-level audit with three outcomes: supported under tested assumptions, reversed by a specific measurement choice, or blocked by missing evidence. Paired translated headlines reveal disagreement between language-specific instruments. Controlled engineering and semantic streams let us measure false releases, retained correct claims, and release rate against separate references. The audit lowers false-release rates on semantic tracks but abstains too often on the engineering track. The released artifact makes these tradeoffs reproducible. Agreement across specifications shows robustness to the tested checks; it does not prove semantic correctness or accuracy on unlabeled news titles.