16 citations as recorded by crossref.

1. Evaluating the feasibility of using artificial neural networks to predict lithofacies in complex glacial deposits Z. Hammond & D. Allen 10.1007/s10040-023-02726-2
2. 3D lithological mapping of borehole descriptions using word embeddings I. Fuentes et al. 10.1016/j.cageo.2020.104516
3. Classification of geological borehole descriptions using a domain adapted large language model H. Ghorbanfekr et al. 10.1016/j.acags.2025.100229
4. Machine Learning and Artificial Intelligence Applications in Soil Science B. Minasny & A. McBratney 10.1111/ejss.70093
5. Portuguese word embeddings for the oil and gas industry: Development and evaluation D. Gomes et al. 10.1016/j.compind.2020.103347
6. Deep learning text classification of borehole logs for regional scale modeling of hydrofacies (Po Plain, N Italy) A. Previati et al. 10.1016/j.ejrh.2024.102157
7. Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling C. Lawley et al. 10.1007/s11053-023-10216-1
8. Geoscience language models and their intrinsic evaluation C. Lawley et al. 10.1016/j.acags.2022.100084
9. dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs R. Joshi et al. 10.5194/gmd-14-6711-2021
10. Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing? M. Häberle et al. 10.1016/j.isprsjprs.2022.04.006
11. Potential of natural language processing for metadata extraction from environmental scientific publications G. Blanchy et al. 10.5194/soil-9-155-2023
12. A novel few-shot learning framework for rock images dually driven by data and knowledge Z. Chen et al. 10.1016/j.acags.2024.100155
13. Artificial intelligence in soil science A. Wadoux 10.1111/ejss.70080
14. Machine Learning-Based Prospective Modeling for Alluvial Gold Mining: A Study Area in Colombia F. Bertaiola et al. 10.1007/s00024-025-03830-y
15. Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations Z. Ma & G. Mei 10.1016/j.jrmge.2024.02.034
16. Word embeddings for application in geosciences: development, evaluation, and examples of soil-related concepts J. Padarian & I. Fuentes 10.5194/soil-5-177-2019