19 citations as recorded by crossref.

1. Taxonomic review of living planktonic foraminifera G. Brummer & M. Kučera https://doi.org/10.5194/jm-41-29-2022
2. Identification of living (Rose Bengal)-stained benthic foraminifera using automated image recognition T. Walla et al. https://doi.org/10.5194/jm-45-623-2026
3. Southern California margin benthic foraminiferal assemblages record recent centennial-scale changes in oxygen minimum zone H. Palmer et al. https://doi.org/10.5194/bg-17-2923-2020
4. Bridging the extant and fossil record of planktonic foraminifera: implications for the Globigerina lineage A. Fabbrini et al. https://doi.org/10.1111/pala.12676
5. Representing Zooplankters: An Example from the Foraminifera G. Scott https://doi.org/10.3390/geosciences14060169
6. AI, citizen science data and expert opinions: towards an improved fossil identification process I. Eijkelboom et al. https://doi.org/10.7717/peerj-cs.4049
7. Small but mighty: how overlooked small species maintain community structure through middle Eocene climate change L. Kearns et al. https://doi.org/10.1017/pab.2022.24
8. Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning J. Sun et al. https://doi.org/10.7717/peerj.16200
9. Diachroneity Rules the Mid-Latitudes: A Test Case Using Late Neogene Planktic Foraminifera across the Western Pacific A. Lam et al. https://doi.org/10.3390/geosciences12050190
10. Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks X. Liu et al. https://doi.org/10.1017/pab.2022.14
11. Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks A. Hsiang et al. https://doi.org/10.1029/2019PA003612
12. A new radiolarian transfer function for the Pacific Ocean and application to fossil records: Assessing potential and limitations for the last glacial-interglacial cycle I. Hernández-Almeida et al. https://doi.org/10.1016/j.gloplacha.2020.103186
13. Factors affecting consistency and accuracy in identifying modern macroperforate planktonic foraminifera I. Fenton et al. https://doi.org/10.5194/jm-37-431-2018
14. The impact of artificial intelligence systems in micropalaeontology F. Marret https://doi.org/10.1016/j.eve.2023.100022
15. Planktonic foraminifera in biostratigraphy and biochronology M. Petrizzo et al. https://doi.org/10.1127/nos/2024/0840
16. Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks X. Liu & H. Song https://doi.org/10.1016/j.sedgeo.2020.105790
17. Machine Learning for identification and classification of Foraminifera: Testing on monothalamids A. Sabbatini et al. https://doi.org/10.1016/j.marmicro.2025.102442
18. Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts I. Martinsen et al. https://doi.org/10.1016/j.aiig.2025.100145
19. Automated identification of fossil benthic foraminifera from the Peruvian margin using convolutional neural networks S. Hayat et al. https://doi.org/10.5194/jm-45-95-2026