26 citations as recorded by crossref.

1. Linking zooplankton time series to the fossil record L. Jonkers et al. 10.1093/icesjms/fsab123
2. An Improved Cleaning Protocol for Foraminiferal Calcite from Unconsolidated Core Sediments: HyPerCal—A New Practice for Micropaleontological and Paleoclimatic Proxies S. Zarkogiannis et al. 10.3390/jmse8120998
3. Species-Level Microfossil Prediction for Globotruncana genus Using Machine Learning Models K. Gorur et al. 10.1007/s13369-022-06822-5
4. Glacial expansion of carbon-rich deep waters into the Southwestern Indian Ocean over the last 630 kyr J. Pérez-Asensio et al. 10.1016/j.gloplacha.2023.104283
5. Automated detection of microfossil fish teeth from slide images using combined deep learning models K. Mimura et al. 10.1016/j.acags.2022.100092
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7. Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (polycystine radiolaria) V. Carlsson et al. 10.5194/jm-41-165-2022
8. Towards a Fleet of Robots for Orientation, Imaging, and Morphometric Analyses of Planktonic Foraminifera M. Knappertsbusch & J. Eisenecker 10.3389/fmars.2022.798002
9. Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach Y. Hou et al. 10.5194/jm-40-163-2021
10. Forabot: Automated Planktic Foraminifera Isolation and Imaging T. Richmond et al. 10.1029/2022GC010689
11. Merging databases for CNN image recognition, increasing bias or improving results? M. Tetard et al. 10.1016/j.marmicro.2023.102296
12. A NEW MODEL ON BENTHIC FORAMINIFER IMAGE CLASSIFICATION AND DEFINITIONS BASED ON CONVENTIONAL NEURAL NETWORK (CNN) K. YAYAN & U. YAYAN 10.31796/ogummf.1096951
13. Environmental Controls of Size Distribution of Modern Planktonic Foraminifera in the Tropical Indian Ocean M. Adebayo et al. 10.1029/2022GC010586
14. Temporal changes in zooplankton indicators highlight a bottom-up process in the Bay of Marseille (NW Mediterranean Sea) T. Garcia et al. 10.1371/journal.pone.0292536
15. Artificial intelligence in paleontology C. Yu et al. 10.1016/j.earscirev.2024.104765
16. Automatic identification of conodont species using fine-grained convolutional neural networks X. Duan 10.3389/feart.2022.1046327
17. Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes M. Adaïmé et al. 10.1093/pnasnexus/pgad419
18. Towards investigation of transfer learning framework for Globotruncanita genus and Globotruncana genus microfossils in Genus-Level and Species-Level prediction I. Ozer et al. 10.1016/j.jestch.2023.101589
19. Convolutional neural network application on a new middle Eocene radiolarian dataset V. Carlsson et al. 10.1016/j.marmicro.2023.102268
20. A comparative approach employing microCT for the analysis of Cenozoic foraminifera from the Brazilian carbonate equatorial platform O. De Araújo et al. 10.1016/j.micron.2024.103611
21. ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis I. Ferreira-Chacua & A. Koeshidayatullah 10.1109/ACCESS.2023.3291620
22. Morphometrics and machine learning discrimination of the middle Eocene radiolarian species Podocyrtis chalara, Podocyrtis goetheana and their morphological intermediates F. Pinto et al. 10.1016/j.marmicro.2023.102293
23. Eubuliminella tenuata as a new proxy for quantifying past bottom water oxygenation M. Tetard et al. 10.1016/j.marmicro.2021.102016
24. 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
25. From fossils to mind A. de Sousa et al. 10.1038/s42003-023-04803-4
26. Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow M. Tetard et al. 10.5194/cp-16-2415-2020