12 citations as recorded by crossref.

1. Taxonomic review of living planktonic foraminifera G. Brummer & M. Kučera 10.5194/jm-41-29-2022
2. Southern California margin benthic foraminiferal assemblages record recent centennial-scale changes in oxygen minimum zone H. Palmer et al. 10.5194/bg-17-2923-2020
3. Bridging the extant and fossil record of planktonic foraminifera: implications for the Globigerina lineage A. Fabbrini et al. 10.1111/pala.12676
4. Small but mighty: how overlooked small species maintain community structure through middle Eocene climate change L. Kearns et al. 10.1017/pab.2022.24
5. Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning J. Sun et al. 10.7717/peerj.16200
6. Diachroneity Rules the Mid-Latitudes: A Test Case Using Late Neogene Planktic Foraminifera across the Western Pacific A. Lam et al. 10.3390/geosciences12050190
7. Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks X. Liu et al. 10.1017/pab.2022.14
8. Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks A. Hsiang et al. 10.1029/2019PA003612
9. 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. 10.1016/j.gloplacha.2020.103186
10. Factors affecting consistency and accuracy in identifying modern macroperforate planktonic foraminifera I. Fenton et al. 10.5194/jm-37-431-2018
11. The impact of artificial intelligence systems in micropalaeontology F. Marret 10.1016/j.eve.2023.100022
12. Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks X. Liu & H. Song 10.1016/j.sedgeo.2020.105790