7 citations as recorded by crossref.

1. Artificial intelligent identification of apatite fission tracks based on machine learning Z. Ren et al. 10.1088/2632-2153/ad0e17
2. How many vs which: On confined track selection criteria for apatite fission track analysis M. Tamer & R. Ketcham 10.1016/j.chemgeo.2023.121584
3. Short communication: Experimental factors affecting fission-track counts in apatite C. Aslanian et al. 10.5194/gchron-4-109-2022
4. Visual Interpretation of Machine Learning: Genetical Classification of Apatite from Various Ore Sources T. Zhou et al. 10.3390/min13040491
5. Automatic identification of semi-tracks on apatite and mica using a deep learning method R. Li et al. 10.1016/j.cageo.2022.105081
6. Zircon classification from cathodoluminescence images using deep learning D. Zheng et al. 10.1016/j.gsf.2022.101436
7. Technical note: colab_zirc_dims: a Google Colab-compatible toolset for automated and semi-automated measurement of mineral grains in laser ablation–inductively coupled plasma–mass spectrometry images using deep learning models M. Sitar & R. Leary 10.5194/gchron-5-109-2023