contentEffort

A language model estimates how much editorial effort and depth went into a page. The system distinguishes between shallow, machine-generated content and pages with recognizable research, structure and substance. It is not a hard measurement but a model estimate — one that increasingly reliably separates thin AI content from genuine editorial engagement.

A single AI-estimated value distilled from the page's text content. The model was trained to recognize effort and depth. Its output feeds into NSR.

A single LLM-estimated value for creation effort/depth — not a raw measurement but a model estimate; separates substance from thin AI output. [B field, C scale]

Expertise is primary — high creation effort is a sign of subject-matter engagement. Experience is indirect because effortful content often also contains first-hand material. Trust is indirect.

Automatically generated pages without editorial added value are at a disadvantage compared to pages with context and explanation. A report that explains what an event means for those affected earns more contentEffort points than one that only lists raw data.