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Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb...

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Detalles Bibliográficos
Autores principales:	Love, Natalie L. R., Bonnet, Pierre, Goëau, Hervé, Joly, Alexis, Mazer, Susan J.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623300/ https://www.ncbi.nlm.nih.gov/pubmed/34834835 http://dx.doi.org/10.3390/plants10112471

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