What best describes coverage mode in Review Center?

Coverage mode in Review Center is designed specifically to focus on serving documents that are optimal for training the model. This mode aims to provide a comprehensive dataset that helps in refining the model's predictive capabilities. By presenting a diverse array of documents that may vary in relevance, it improves the model's understanding and effectiveness in differentiating between relevant and irrelevant content.

In this mode, the emphasis is on creating the best training environment for the model by exposing it to various document types and contexts, which in turn enhances its ability to accurately identify relevant documents in future reviews. The objective here is not just to find documents that are likely to be relevant but to ensure that the model is well-trained with a rich set of examples.

The other options do not accurately describe coverage mode. For example, while some features might focus on finding documents likely to be relevant or have a certain relevance ranking, the primary goal of coverage mode is distinctively tied to optimal training rather than simply relevance or volume. Therefore, understanding the function of coverage mode in the context of model training is essential for leveraging its capabilities effectively within Review Center.