Author

D. Curnick

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:ecology & biodiversity· 1 Assay:imaging· 1 Organism:multiple species· 1

Frequent Co-Authors

A. Haguenauer· 1 shared A. Hoey· 1 shared B. Williams· 1 shared C. Webster· 1 shared E. Weschke· 1 shared F. Zuberer· 1 shared J. A. Tobias· 1 shared J. Barker· 1 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperEcology

A. Haguenauer

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2 papers

Recent·Most cited

A trait-based metric to prioritise the conservation of functionally irreplaceable species at risk of extinction

C. Webster, J. Barker, D. Curnick, M. Gollock, J. Hansford, M. Hoffmann, T. E. Martin, T. J. Matthews, N. Pettorelli, J. A. Tobias, S. T. Turvey, P. A. Walkden, J. Wang, J. P. Wayman, J. Rosindell, R. Gumbs

This paper introduces a metric called FIRE to prioritize conservation of functionally irreplaceable species at risk of extinction by integrating trait data and extinction risk.

bioRxiv · Jun 5, 2024 · PreprintEcology

Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win out

B. Williams, S. M. Balvanera, S. S. Sethi, T. A. C. Lamont, J. Jompa, M. Prasetya, L. Richardson, E. Weschke, A. Hoey, R. Beldade, S. C. Mills, A. Haguenauer, F. Zuberer, S. D. Simpson, D. Curnick, K. E. Jones

This paper demonstrates that pretrained neural networks and unsupervised learning effectively analyze coral reef soundscapes for ecological insights without manual annotation.

bioRxiv · Feb 2, 2024Ecology