Articles | Volume 4, issue 1
https://doi.org/10.5194/gchron-4-269-2022
https://doi.org/10.5194/gchron-4-269-2022
Research article
 | 
18 May 2022
Research article |  | 18 May 2022

Improving age–depth relationships by using the LANDO (“Linked age and depth modeling”) model ensemble

Gregor Pfalz, Bernhard Diekmann, Johann-Christoph Freytag, Liudmila Syrykh, Dmitry A. Subetto, and Boris K. Biskaborn

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Cited articles

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A system for large-scale machine learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2–4 November 2016, Savannah, GA, USA, 265–283, https://doi.org/10.1016/0076-6879(83)01039-3, 2016. 
Abbott, M. B. and Stafford, T. W.: Radiocarbon geochemistry of modern and Ancient Arctic lake systems, Baffin Island, Canada, Quat. Res., 45, 300–311, https://doi.org/10.1006/qres.1996.0031, 1996. 
Alasadi, S. A. and Bhaya, W. S.: Review of data preprocessing techniques in data mining, J. Eng. Appl. Sci., 12, 4102–4107​​​​​​​, 2017. 
Anderson, P., Minyuk, P., Lozhkin, A., Cherepanova, M., Borkhodoev, V., and Finney, B.: A multiproxy record of Holocene environmental changes from the northern Kuril Islands (Russian Far East), J. Paleolimnol., 54, 379–393, https://doi.org/10.1007/s10933-015-9858-y, 2015. 
Anderson, P. M. and Lozhkin, A. V.: Late Quaternary vegetation of Chukotka (Northeast Russia), implications for Glacial and Holocene environments of Beringia, Quat. Sci. Rev., 107, 112–128, https://doi.org/10.1016/j.quascirev.2014.10.016, 2015. 
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Short summary
We use age–depth modeling systems to understand the relationship between age and depth in lake sediment cores. However, depending on which modeling system we use, the model results may vary. We provide a tool to link different modeling systems in an interactive computational environment and make their results comparable. We demonstrate the power of our tool by highlighting three case studies in which we test our application for single-sediment cores and a collection of multiple sediment cores.