anchor.diff:          "match_all": {}
         },
         "script": {
  -        "source": "dotProduct(params.queryVector, doc['my_dense_vector'])",
  +        "source": """
  +          double value = dotProduct(params.query_vector, doc['my_vector']);
  +          return sigmoid(1, Math.E, -value); <1>
  +        """,
           "params": {
  -          "queryVector": [4, 3.4, -0.2]
  +          "query_vector": [4, 3.4, -0.2]
           }
         }
       }
  
positive.code: }
  --------------------------------------------------
  // NOTCONSOLE
  <1> To take advantage of the script optimizations, provide a query vector as a script parameter.
  
  Similarly, for sparse_vector fields, `cosineSimilaritySparse` calculates cosine similarity
  between a given query vector and document vectors.
  
negative.code: // CONSOLE
  // TEST[setup:twitter]
  
  NOTE: The values returned from `script_score` cannot be negative. In general,
  Lucene requires the scores produced by queries to be non-negative in order to
  support certain search optimizations.
  
  ==== Accessing the score of a document within a script
  
  Within a script, you can
  