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The method of rating elastic query outcomes based on constantly changing derived characteristics of documents is called "dynamic relevance ranking" or "dynamic query optimization." This approach involves continuously updating the relevance ranking of documents based on various factors such as user behavior, search context, and content changes. It uses machine learning algorithms and other advanced techniques to improve the precision and recall of search results by adapting to the evolving needs of users and the content being searched. Ultimately, dynamic relevance ranking helps deliver more accurate and personalized search results that reflect the most up-to-date information available.