Reports

Test Title

Author 1, Author 2

The ReID-Wild project was initiated in 2025 as a collaborative effort led by researchers committed to advancing wildlife monitoring and conservation technologies. Bringing together a multidisciplinary team spanning computer vision, ecology, and biodiversity management, the project focuses on developing robust, scalable tools for individual stoat identification in real-world environments.

The programme leverages state-of-the-art machine learning methods and close partnerships with conservation organisations to support predator management and long-term ecological monitoring. Building on prior investment in field-camera infrastructure and ecological datasets, ReID-Wild aims to enable accurate, automated, and ethical identification of individual stoats, ultimately supporting data-driven strategies to protect vulnerable ecosystems.

The ReID-Wild project was initiated in 2025 as a collaborative effort led by

Test Title

Author 1, Author 2

The ReID-Wild project was initiated in 2025 as a collaborative effort led by researchers committed to advancing wildlife monitoring and conservation technologies. Bringing together a multidisciplinary team spanning computer vision, ecology, and biodiversity management, the project focuses on developing robust, scalable tools for individual stoat identification in real-world environments.

The programme leverages state-of-the-art machine learning methods and close partnerships with conservation organisations to support predator management and long-term ecological monitoring. Building on prior investment in field-camera infrastructure and ecological datasets, ReID-Wild aims to enable accurate, automated, and ethical identification of individual stoats, ultimately supporting data-driven strategies to protect vulnerable ecosystems.

The ReID-Wild project was initiated in 2025 as a collaborative effort led by

Publications

Qiao, T. (2025, September). Longitudinal Surveys Are Texts: LLM-Enhanced Analysis of School Attendance in New Zealand. Machine Learning and Knowledge Discovery in Databases ECML PKDD Part VIII (pp 310–327). European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Porto.