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Furner, R., Haynes, P., Jones, DC., Munday, D., Paige, B. and Shuckburgh, E., 2025. The challenge of land in a neural network ocean model Environmental Data Science, v. 3
Doi: 10.1017/eds.2024.49
Zuñiga-Gonzalez, A., Madhavapeddy, A. and Bardhan, R., 2025. Green Urban Equity: Analyzing the 3-30-300 Rule in UK Cities and Its Socioeconomic Implications
Doi: 10.5194/egusphere-egu24-20833
Eyres, A., Ball, TS., Dales, M., Swinfield, T., Arnell, A., Baisero, D., Durán, AP., Green, JMH., Green, RE., Madhavapeddy, A. and Balmford, A., 2025. LIFE: A metric for mapping the impact of land-cover change on global extinctions Philosophical Transactions of the Royal Society B: Biological Sciences, v. 380
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Nivron, O., Wischik, DJ., Vrac, M., Shuckburgh, E. and Archibald, AT., 2025. A temporal stochastic bias correction using a machine learning attention model Environmental Data Science, v. 3
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Virdee, M., Kazlauskaite, I., Boland, EJD., Shuckburgh, E. and Ming, A., 2025. Spatial and temporal dependence in distribution-based evaluation of CMIP6 daily maximum temperatures Atmospheric Science Letters, v. 26
Doi: http://doi.org/10.1002/asl.1290
Rouse, RE., Khamis, D., Hosking, S., McRobie, A. and Shuckburgh, E., 2025. Streamflow prediction using artificial neural networks and soil moisture proxies Environmental Data Science, v. 4
Doi: http://doi.org/10.1017/eds.2024.48
2024 (Published online)
Collins, M., Beverley, JD., Bracegirdle, TJ., Catto, J., McCrystall, M., Dittus, A., Freychet, N., Grist, J., Hegerl, GC., Holland, PR., Holmes, C., Josey, SA., Joshi, M., Hawkins, E., Lo, E., Lord, N., Mitchell, D., Monerie, P-A., Priestley, MDK., Scaife, A., Screen, J., Senior, N., Sexton, D., Shuckburgh, E., Siegert, S., Simpson, C., Stephenson, DB., Sutton, R., Thompson, V., Wilcox, LJ. and Woollings, T., 2024 (Published online). Emerging signals of climate change from the equator to the poles: new insights into a warming world Frontiers in Science, v. 2
Doi: http://doi.org/10.3389/fsci.2024.1340323
2024 (No publication date)
2024
Wilkins, G., Keshav, S. and Mortier, R., 2024. Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems ACM SIGEnergy Energy Informatics Review, v. 4
Doi: 10.1145/3727200.3727217
Swinfield, T., Shrikanth, S., Bull, JW., Madhavapeddy, A. and zu Ermgassen, SOSE., 2024. Nature-based credit markets at a crossroads Nature Sustainability, v. 7
Doi: 10.1038/s41893-024-01403-w
Millar, J., Sethi, S., Haddadi, H. and Madhavapeddy, A., 2024. Poster: Towards Low-Power Comprehensive Biodiversity Monitoring SenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems,
Doi: http://doi.org/10.1145/3666025.3699400
Nath, P., Moss, H., Shuckburgh, E. and Webb, M., 2024. RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models. CoRR, v. abs/2408.16118
