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. The value is not a hard measurement but a model estimate, one that increasingly reliably separates thin AI content from genuine editorial engagement.

The data is the page's text content. From it, a model distills a single AI-estimated value. The model was trained to recognize effort and depth. Its output feeds into NSR.

This is a single value that an LLM estimates for creation effort and depth. It is not a raw measurement but a model estimate. It 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.