anchor.diff:  "relevant". Defaults to `1`.
   |=======================================================================
   
  +[float]
   ==== Discounted cumulative gain (DCG)
   
   In contrast to the two metrics above, https://en.wikipedia.org/wiki/Discounted_cumulative_gain[discounted cumulative gain] takes both, the rank and the rating of the search results, into account.
  
positive.code: 
  A document `rating` can be any integer value that expresses the relevance of the document on a user defined scale. For some of the metrics, just giving a binary rating (e.g. `0` for irrelevant and `1` for relevant) will be sufficient, other metrics can use a more fine grained scale.  
  
  === Template based ranking evaluation
  
  As an alternative to having to provide a single query per test request, it is possible to specify query templates in the evaluation request and later refer to them. Queries with similar structure that only differ in their parameters don't have to be repeated all the time in the `requests` section this way. In typical search systems where user inputs usually get filled into a small set of query templates, this helps making the evaluation request more succinct.
  
negative.code: <3> a reference to a previously defined temlate
  <4> the parameters to use to fill the template
  
  [float]
  === Available evaluation metrics
  
  The `metric` section determines which of the available evaluation metrics is going to be used.
  Currently, the following metrics are supported:
  
  [float]
  ==== Precision at K (P@k)
  
  This metric measures the number of relevant results in the top k search results. Its a form of the well known https://en.wikipedia.org/wiki/Information_retrieval#Precision[Precision] metric that only looks at the top k documents. It is the fraction of relevant documents in those first k
  