Research
My thesis research utilizes machine learning approaches and field-based experimental techniques to improve our predictions and understanding of the distributions, processes, and mechanisms underlying methylated sulfur cycling in the NE subarctic Pacific and Southern Ocean.
Publications
McNabb, B. J., & Tortell, P. D. (2023). Oceanographic controls on Southern Ocean dimethyl sulfide distributions revealed by machine learning algorithms. Limnology and Oceanography, 1999, 1–15. https://doi.org/10.1002/lno.12298
McNabb, B. J., & Tortell, P. D. (2022). Improved prediction of dimethyl sulfide (DMS) distributions in the northeast subarctic Pacific using machine-learning algorithms. Biogeosciences, 19(6), 1705–1721. https://doi.org/10.5194/bg-19-1705-2022