10 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. Interpreting and reporting fission-track chronological data B. Kohn et al. 10.1130/B37245.1
5. A method for estimating of integral roundness index for a detrital zircons set: a case of the sequences from the Cimmerides of the Mountainous Crimea T. Romanyuk & P. Kotler 10.31857/S0024497X24030041
6. Visual Interpretation of Machine Learning: Genetical Classification of Apatite from Various Ore Sources T. Zhou et al. 10.3390/min13040491
7. Automatic identification of semi-tracks on apatite and mica using a deep learning method R. Li et al. 10.1016/j.cageo.2022.105081
8. Zircon classification from cathodoluminescence images using deep learning D. Zheng et al. 10.1016/j.gsf.2022.101436
9. 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
10. Method of Estimating the Integral Roundness Index for Detrital Zircons: A Case Study of Cimmeride Sedimentary Sequences in the Crimean Mountains T. Romanyuk & P. Kotler 10.1134/S0024490224700524