Author

N. P. Snow

Animal and Plant Health Inspection Service

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· 4

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:ecology & biodiversity· 1 Approach:experimental assay· 1 Organism:multiple species· 1

Frequent Co-Authors

R. S. Miller· 4 shared K. C. VerCauteren· 3 shared M. A. Tabak· 3 shared J. S. Lewis· 2 shared P. E. Schlichting· 2 shared R. K. Boughton· 2 shared R. K. Brook· 2 shared A. K. Moeller· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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4 shared papersEcology

R. S. Miller

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Operational lessons learned from simulating an elimination response to a transboundary animal disease in wild animals

Kayleigh Chalkowski, Kim M. Pepin, Michael J. Lavelle, R. S. Miller, Justin W. Fischer, V. R. Brown, Michael P. Glow, B. Smith, S. Cook, Keely J. Kohen, Sarah Sherburne, H. Smith, Bruce R. Leland, Kurt C. VerCauteren, N. P. Snow

The paper simulates a TAD control effort in wild pigs to identify operational challenges and propose solutions for rapid population reduction during a hypothetical outbreak.

Preventive Veterinary Medicine · Jul 23, 2024Peer-reviewedEcology

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

M. A. Tabak, D. Falbel, T. Hamzeh, R. K. Brook, J. A. Goolsby, L. D. Zoromski, R. K. Boughton, N. P. Snow, K. C. VerCauteren, R. S. Miller

CameraTrapDetectoR uses deep learning to detect, classify, and count animals in camera trap images, achieving high accuracy across multiple taxonomic levels.

bioRxiv · Feb 7, 2022 · PreprintEcology

rapidPop: Rapid population assessments of wildlife using camera trap data in R Shiny Applications

M. A. Tabak, J. S. Lewis, P. E. Schlichting, N. P. Snow, K. C. VerCauteren, R. S. Miller

The paper introduces an R package for rapid population assessments using camera trap data, enabling occupancy modeling with Shiny applications to analyze density changes.

bioRxiv · Mar 30, 2020 · PreprintEcology

Machine learning to classify animal species in camera trap images: applications in ecology

M. A. Tabak, M. S. Norouzzadeh, D. W. Wolfson, S. J. Sweeney, K. C. VerCauteren, N. P. Snow, J. M. Halseth, P. A. Di Salvo, J. S. Lewis, M. D. White, B. Teton, J. C. Beasley, P. E. Schlichting, R. K. Boughton, B. Wight, , R. S. Miller

This paper trains a machine learning model to classify animal species in camera trap images, achieving high accuracy and providing an R package for ecological use.

bioRxiv · Jul 9, 2018Ecology