Short topic description using GPT model.

<p>A brief topic description was generated using GPT-3.5 on the keywords and representative documents from the BERTopic model. Topic (-1) includes outliers. The count is the number of representative documents. Representation is the summarization of the representative documents.</p>

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Bibliographic Details
Main Author:	Julia C. Lensing (20521847) (author)
Other Authors:	John Y. Choe (20390917) (author), Branden B. Johnson (6312122) (author), Jingwen Wang (219000) (author)
Published:	2025
Subjects:	Biotechnology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified published daily worldwide made publicly available level emergency management gpt ), offer disrupt lifeline infrastructures survey results highlight known risk associated allowing future generations reveals potential differences potential user groups div >< p practical disaster reports case study illustrates practical reports future disasters case study brief survey vital resources various forms trained transformer text mining supporting data response plans resiliency plans reports serve primarily interpretable practice due past events pacific northwest oregon state limited impact impact assessments generative pre explore opportunities experienced professionals ensure reproducibility challenges associated better mitigate action reports
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Short topic description using GPT model.

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