Journal of Micropalaeontology
Journal of Micropalaeontology
JM
Articles | Volume 41, issue 2
Articles | Volume 41, issue 2
https://doi.org/10.5194/jm-41-165-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/jm-41-165-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 41, issue 2
Research article
 | 
04 Nov 2022
Research article |  | 04 Nov 2022

Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (polycystine radiolaria)

Veronica Carlsson, Taniel Danelian, Pierre Boulet, Philippe Devienne, Aurelien Laforge, and Johan Renaudie

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Cited articles

Aitchison, J. C., Suzuki, N., Caridroit, M., Danelian, T., and Noble, P.: Paleozoic radiolarian biostratigraphy, in: Catalogue of Paleozoic Radiolarian Genera, edited by: Danelian, T., Caridroit, M., Noble, P., and Aitchison, J. C., Geodiversitas, Vol. 39, 503–531, Scientific Publications of the Muséum National d'Histoire Naturelle, Paris, https://doi.org/10.5252/g2017n3a5, 2017. 
Beaufort, L. and Dollfus, D.: Automatic recognition of coccoliths by dynamical neural networks, Mar. Micropaleontol., 51, 57–73, https://doi.org/10.1016/j.marmicro.2003.09.003, 2004. 
Beaufort, L., de Garidel Thoron, T., Mix, A. C., and Pisias, N. G.: ENSO-like forcing on Oceanic Primary Production during the late Pleistocene, Science, 293, 2440–2444, https://doi.org/10.1126/science.293.5539.2440, 2001. 
Brocher, J.: biovoxxel/BioVoxxel-Toolbox: BioVoxxel Toolbox (v2.5.3), Zenodo, https://doi.org/10.5281/zenodo.5986129, 2022. 
Carlsson, V.: Podocyrtis Image Dataset, Recherche Data Gouv [data set], https://doi.org/10.57745/G7CHQL, 2022. 
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This study evaluates the use of automatic classification using AI on eight closely related radiolarian species of the genus Podocyrtis based on MobileNet CNN. Species belonging to Podocyrtis are useful for middle Eocene biostratigraphy. Numerous images of Podocyrtis species from the tropical Atlantic Ocean were used to train and validate the CNN. An overall accuracy of about 91 % was obtained. Additional Podocyrtis specimens from other ocean realms were used to test the predictive model.
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