<｜begin▁of▁sentence｜>### Instruction:
As a data scientist, you are working on a project that involves analyzing a network of college football teams. You have a file containing the network data in the 'football.gml' format, and you need to perform community detection using an algorithm that utilizes the amplified commute distance as the distance metric. Additionally, you must specify parameters for the algorithm, including the number of clusters (5) and a threshold of 0.3. After running the community detection, you need to generate and print the JSON representation of the detected communities.

You can achieve this by using the LPAM algorithm from the cdlib library, which provides a method for conducting community detection on networks. After obtaining the detected communities, you'll need to convert and print the results in JSON format for further analysis and interpretation.
### Response:
from cdlib import algorithms
import networkx as nx

# Load the American College football graph
G = nx.read_gml('football.gml')

# Perform community detection using Link Partitioning Around Medoids (LPAM)
coms = algorithms.lpam(G, k=5, threshold=0.3, distance='amp')

# Get JSON representation of the detected communities
coms_json = coms.to_json()

print(coms_json)
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