<｜begin▁of▁sentence｜>### Instruction:
As a Project Scheduler responsible for managing the development of a new power grid infrastructure project, you're tasked with ensuring efficient community detection within the network structure to optimize resource allocation and project progress tracking. The power grid data, stored in a GML file named "power.gml," contains valuable insights into the network's topology.

To facilitate community detection, you're looking to utilize the threshold_clustering function provided by the cdlib library. Specifically, you aim to employ the mean of the out-ties within the power grid graph as the threshold function for identifying communities.

Once the community structure is identified, you need to output the results in JSON format for easy visualization and integration with other project management tools.

Your objective is to streamline the project's execution by leveraging network analysis techniques to enhance resource allocation and collaboration among different segments of the power grid development team.
### Response:
import networkx as nx
from cdlib import algorithms
import numpy as np

# Create a graph
G = nx.read_gml('power.gml')

# Apply threshold_clustering algorithm
# In this example, we use the mean of the out-ties as the threshold function
communities = algorithms.threshold_clustering(G, threshold_function=np.mean)

# Convert clustering to JSON
clustering_json = communities.to_json()

print(clustering_json)
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