What does the Prioritized Review queue serve up until the first model is built and documents are ranked?

The Prioritized Review queue is designed to assist reviewers in efficiently examining documents during the early stages of the analytics process, specifically before the initial model is developed and documents are appropriately ranked. While waiting for the model, the queue serves up random documents. This approach allows reviewers to start their assessments without relying on prioritized ranking systems, which are not yet available.

Randomly assigning documents ensures that no specific bias or order influences the review process at this stage. It enables a diverse selection of documents to be reviewed, which is important for building a comprehensive understanding of the data set, even when analytical rankings are not yet established. In the context of machine learning and analytics, beginning with a random selection can help ensure a well-rounded input for subsequent modeling and ranking processes as more information becomes available.

The other options suggest particular ordering or selection criteria, which do not apply when the initial model has yet to be created. Thus, the focus on random document selection during this phase highlights an effective strategy for working with documents while awaiting more refined analytics.