anchor.diff:  ------------------------------
   // NOTCONSOLE
   
  -<1> a set of typical search requests to your system
  +<1> a set of typical search requests, together with their provided ratings
   <2> definition of the evaluation metric to calculate
   <3> a specific metric and its parameters
   
  
positive.code: [cols="<,<",options="header",]
  |=======================================================================
  |Parameter |Description
  |`relevant_rating_threshold` |Sets the rating threshold from which on documents are considered to be
  "relevant". Defaults to `1`.
  |`ignore_unlabeled` |controls how unlabeled documents in the search results are counted.
  If set to 'true', unlabeled documents are ignored and neither count as relevant or irrelevant. Set to 'false' (the default), they are treated as irrelevant.
  
negative.code: The `metric` section determines which of the available evaluation metrics is going to be used.
  Currently, the following metrics are supported:
  
  ==== 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
  search. A precision at 10 (P@10) value of 0.6 then means six out of the 10 top hits are relevant with respect to the users information need.
  
  P@k works well as a simple evaluation metric that has the benefit of being easy to understand and explain.
  Documents in the collection need to be rated either as relevant or irrelevant with respect to the current query. 
  P@k does not take into account where in the top k results the relevant documents occur, so a ranking of ten results that 
  contains one relevant result in position 10 is equally good as a ranking of ten results that contains one relevant result in position 1.
  
  [source,js]
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