anchor.diff:  start(Params, [TreeId | ExtraIds], Tree1OpenOrEmpty, Tree2OpenOrEmpty) ->
       {Segments, Width, MemLevels} = Params,
       %% Return now so we can store symbolic value in procdict in next_state call
  -    T1A = create_and_open_hashtree(TreeId, Segments, Width, MemLevels, Tree1OpenOrEmpty, ExtraIds),
  +    T1A = create_and_open_hashtree(TreeId, Segments, Width, MemLevels,
  +        Tree1OpenOrEmpty, ExtraIds),
   
       put(t1, T1A),
   
  -    %% TODO: Can we just use Segments, Width, Memlevels?
  -    T2A = create_and_open_hashtree(TreeId, hashtree:segments(T1A), hashtree:width(T1A), 
  -        hashtree:mem_levels(T1A), Tree2OpenOrEmpty, ExtraIds),
  +    T2A = create_and_open_hashtree(TreeId, Segments, Width, MemLevels,
  +        Tree2OpenOrEmpty, ExtraIds),
   
       put(t2, T2A),
   
  
positive.code: %%
  %% N.B. This does not work with memory levels enabled as update_perform uses the
  %% snapshot state which is dumped.
  %% JDM: Is this a limitation of the model, or hashtree?
  update_snapshot(T, S) ->
      %% Snapshot the hashtree and store both states
      {SS, HT} = hashtree:update_snapshot(get(T)),
  
negative.code: next_state(S,_R,{call, _, local_compare1, []}) ->
      S.
  
  
  %% Property to generate a series of commands against the
  %% hashtrees and afterwards force them to a comparable state.
  