anchor.diff:  
   Influencer results show which entities were anomalous and when.
   For example, at 16:05 `user_name: Bob` was unusual.
  -This is a summary of all anomalies for each entity, so there can be a lot of these results. 
  -Once you have identified a noteable bucket time, you can look to see which entites were significant.
  +This is a summary of all anomalies for each entity, so there can be a lot of these results.
  +Once you have identified a notable bucket time, you can look to see which entites were significant.
   
   Record results provide the detail showing what the individual anomaly was, when it occurred and which entity was involved.
   For example, at 16:05 Bob sent 837262434 bytes, when the typical value was 1067 bytes.
  -Once you have identifed a bucket time and/or a significant entity, you can drill through to the record results 
  +Once you have identified a bucket time and/or a significant entity, you can drill through to the record results
   in order to investigate the anomalous behavior.
   
   //TBD Add links to categorization
   Categorization results contain the definitions of _categories_ that have been identified.
  -These are only applicable for jobs that are configured to analyze unstructured log data using categorization. 
  +These are only applicable for jobs that are configured to analyze unstructured log data using categorization.
   These results do not contain a timestamp or any calculated scores.
   
   * <<ml-results-buckets,Buckets>>
  
positive.code: 
  `initial_anomaly_score`::
    (number) The score between 0-100 for each bucket influencers.
    This is this initial value calculated at the time the bucket was processed. 
  
  `influencer_field_name`::
    (string) The field name of the influencer. For example `client_ip` or `user_name`.
  
negative.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.
  
  
  