anchor.diff:    (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. 
  +  (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, 
  +  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
  
positive.code: `timestamp`::
    (date) The start time of the bucket for which these results have been calculated for.
  
  NOTE: Additional influencer properties are added, depending on the fields being analyzed. 
  For example, if analysing `user_name` as an influencer, then a field `user_name` would be added to the 
  result document. This allows easier filtering of the anomaly results.
  
  
  
negative.code: Each bucket has an `anomaly_score`, which is a statistically aggregated and
  normalized view of the combined anomalousness of all record results within each bucket.
  
  One bucket result is written for each `bucket_span` for each job, even if it is not considered to be anomalous
  (when it will have an `anomaly_score` of zero).
  
  Upon identifying an anomalous bucket, you can investigate further by either
  