Author

Joseph W. Millard

Natural History MuseumORCID 0000-0002-3025-3565Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:large language model· 2 Approach:benchmark study· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Daan R. ScheepensiD· 1 shared Drew W. PurvesiD· 1 shared Filip DormiD· 1 shared Maxwell J. FarrelliD· 1 shared Michael HarfootiD· 1 shared Oisin Mac AodhaiD· 1 shared Tim NewboldiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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1 shared paperEcology

Daan R. ScheepensORCID

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2 papers

Recent·Most cited

EcologyEcological InformaticsPeer-reviewedMar 17, 2026

✦ In-depth digest

Large language models possess some ecologicalknowledge, but how much?

Filip Dorm, Joseph W. Millard, Drew W. Purves…·Oisin Mac Aodha

Benchmarking GPT-4o and Gemini 1.5 Pro across five closed-book ecological tasks shows solid coarse species presence and trait recall but poor fine-scale range mapping and threat classification.

18 citationstop 1%

multiple specieslarge language model

𝕏 PostBluesky

EcologyMethods in Ecology and EvolutionPeer-reviewedMay 20, 2024

Large language models help facilitate the automated synthesis of information on potential pest controllers

Daan R. Scheepens, Joseph W. Millard, Maxwell J. Farrell, Tim Newbold

This paper evaluates GPT-4's ability to extract pest and controller information from ecological literature, showing high accuracy in species and location extraction.

36 citations

proteomicsmetagenomics

𝕏 PostBluesky