The first structural candidate is the finite corpus compression regime. Entries 916-920 establish a diagnosis that any research on artificial intelligence, attention economy, or informational ecology will have to confront: the web, after filtration and deduplication, compresses to a nucleus of approximately ten million book-equivalents. This is not a vaguely metaphorical datum. It is a quantifiable hypothesis that admits refutation, refinement, and application. A data science team can operationalize it. An NLP lab can test it. An economist can model its consequences for the marginal value of new knowledge. The idea has operational density because it produces questions: how is that nucleus measured? which texts compose it? how does it vary across disciplines? at what rate does it expand? which algorithms extract it most effectively?