anchor.diff:    For scalability reasons, a maximum of the 10 most significant causes of
     the anomaly will be returned. As part of the core analytical modeling,
     these low-level anomaly records are aggregated for their parent over field record.
  -  The causes resource contains similar elements to the record resource, 
  +  The causes resource contains similar elements to the record resource,
     namely `actual`, `typical`, `*_field_name` and `*_field_value`.
     Probability and scores are not applicable to causes.
   
  
positive.code: `typical`::
    (array) The typical value for the bucket, according to analytical modeling.
  
  NOTE: Additional record properties are added, depending on the fields being analyzed. 
  For example, if analyzing `hostname` as a _by field_, then a field `hostname` would be added to the 
  result document. This allows easier filtering of the anomaly results.
  
  
  
negative.code:   (string) The unique identifier for the job that these results belong to.
  
  `probability`::
    (number) The probability that the influencer has this behavior, in the range 0 to 1.
    For example, 0.0000109783.
    This value can be held to a high precision of over 300 decimal places,
    so the `influencer_score` is provided as a human-readable and friendly interpretation of this.
  // For example, 0.03 means 3%. This value is held to a high precision of over
  //300 decimal places. In scientific notation, a value of 3.24E-300 is highly
  