37 citations as recorded by crossref.

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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. Ten recommendations for scanning foraminifera by X-ray computed tomography A. Searle-Barnes et al. 10.5194/jm-44-107-2025
5. 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
6. Automated detection of microfossil fish teeth from slide images using combined deep learning models K. Mimura et al. 10.1016/j.acags.2022.100092
7. Species-level microfossil identification for globotruncana genus using hybrid deep learning algorithms from the scratch via a low-cost light microscope imaging I. Ozer et al. 10.1007/s11042-022-13810-2
8. 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
9. Foraminiferal image classification based on convolutional neural network considering data augmentation optimization J. Wang et al. 10.1016/j.marmicro.2025.102476
10. Towards a Fleet of Robots for Orientation, Imaging, and Morphometric Analyses of Planktonic Foraminifera M. Knappertsbusch & J. Eisenecker 10.3389/fmars.2022.798002
11. Reviews and syntheses: Review of proxies for low-oxygen paleoceanographic reconstructions B. Hoogakker et al. 10.5194/bg-22-863-2025
12. Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach Y. Hou et al. 10.5194/jm-40-163-2021
13. Forabot: Automated Planktic Foraminifera Isolation and Imaging T. Richmond et al. 10.1029/2022GC010689
14. Merging databases for CNN image recognition, increasing bias or improving results? M. Tetard et al. 10.1016/j.marmicro.2023.102296
15. Advancing paleontology: a survey on deep learning methodologies in fossil image analysis M. Yaqoob et al. 10.1007/s10462-024-11080-y
16. Machine Learning for identification and classification of Foraminifera: Testing on monothalamids A. Sabbatini et al. 10.1016/j.marmicro.2025.102442
17. A NEW MODEL ON BENTHIC FORAMINIFER IMAGE CLASSIFICATION AND DEFINITIONS BASED ON CONVENTIONAL NEURAL NETWORK (CNN) K. YAYAN & U. YAYAN 10.31796/ogummf.1096951
18. Environmental Controls of Size Distribution of Modern Planktonic Foraminifera in the Tropical Indian Ocean M. Adebayo et al. 10.1029/2022GC010586
19. 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
20. Artificial intelligence in paleontology C. Yu et al. 10.1016/j.earscirev.2024.104765
21. Classifying microfossil radiolarians on fractal pre-trained vision transformers K. Mimura et al. 10.1038/s41598-025-90988-z
22. Machine Learning Classification of Fossilized Pectinodon bakkeri Teeth Images: Insights into Troodontid Theropod Dinosaur Morphology J. Bahn et al. 10.3390/make7020045
23. The exploration of the transfer learning technique for Globotruncanita genus against the limited low-cost light microscope images I. Ozer et al. 10.1007/s11760-024-03322-x
24. A machine learning application for the automatic recognition of planktonic foraminifera in thin sections A. Piva et al. 10.1016/j.marpetgeo.2024.106911
25. Automatic identification of conodont species using fine-grained convolutional neural networks X. Duan 10.3389/feart.2022.1046327
26. High resolution 3D images of sediment cores as powerful tool for exploring foraminiferal microhabitats M. Nardelli et al. 10.1016/j.marmicro.2024.102394
27. Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes M. Adaïmé et al. 10.1093/pnasnexus/pgad419
28. Enhanced taxonomic identification of fusulinid fossils through image–text integration using transformer F. Zhang et al. 10.1016/j.cageo.2024.105701
29. 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
30. Convolutional neural network application on a new middle Eocene radiolarian dataset V. Carlsson et al. 10.1016/j.marmicro.2023.102268
31. 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
32. ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis I. Ferreira-Chacua & A. Koeshidayatullah 10.1109/ACCESS.2023.3291620
33. 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
34. Eubuliminella tenuata as a new proxy for quantifying past bottom water oxygenation M. Tetard et al. 10.1016/j.marmicro.2021.102016
35. 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
36. From fossils to mind A. de Sousa et al. 10.1038/s42003-023-04803-4
37. Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow M. Tetard et al. 10.5194/cp-16-2415-2020