<｜begin▁of▁sentence｜>### Instruction:
Imagine our organization needs to enhance its risk evaluation framework by assimilating data from two distinct, well-structured sources. These sources can be visualized as two identical data trees, each perfectly organized with a clear hierarchy of information, following a model where every branch splits into two sub-branches of equal weight – representing our binary tree structure with a height of 4.

Our goal is to consolidate the intelligence from both trees into a single comprehensive system. This new system should maintain the balanced and structured nature of the original sources – a hallmark of efficient data organization. To achieve this, we must introduce a new central node representing the converged point of the two data hierarchies.

The delicate part of the operation is the integration process. We need to make sure that while we are combining these trees with a new commanding node, the resulting tree must retain the characteristics of a fully balanced binary tree, now expanded to a height of 5, to accommodate the additional level.

Furthermore, it's imperative that during this integration, the original identifiers of the data sets – possibly correlating with unique risk identifiers or assessment labels – must be kept intact and reattributed within the new tree structure. A key requirement is that these original identifiers are preserved under a new distinct attribute in our dataset.

No data from the source trees should be lost or conflated during this transition. Think of it as creating an enhanced risk matrix that joins two formerly independent sets of variables into a single, more complex, but navigable system of potential risk factors, all while maintaining an absolute record of their origins.

As for the technical details, if we were to access the schematics of the original data trees, it would likely be provided as a 'gml' file, which would facilitate the manipulation and visualization of these hierarchies to ensure their correct amalgamation.
### Response:
import networkx as nx

# Create two full balanced binary trees with height 4
left_tree = nx.balanced_tree(2, 4)
right_tree = nx.balanced_tree(2, 4)

# Join the two trees with a new root node
# The new root node has the default starting label of 0
joined_tree = nx.join_trees([left_tree, right_tree], label_attribute='old_label', first_label=1)

# Check if the joined tree is isomorphic to a full balanced binary tree of height 5
print(nx.is_isomorphic(joined_tree, nx.balanced_tree(2, 5)))

# Print the edges of the new tree to visualize its structure
print(joined_tree.edges(data=True))
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