Lipid Biosignatures From SO<sub>4</sub>‐Rich Hypersaline Lakes of the Cariboo Plateau

Nichols, Floyd; Pontefract, Alexandra; Dion‐Kirschner, Hannah; Masterson, Andrew L.; Osburn, Magdalena R.

doi:10.1029/2023jg007480

Cited by 2 publications

(2 citation statements)

References 62 publications

(135 reference statements)

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“…The chemistry and geography of these lakes are described in greater detail in Nichols et al. (2023). In short, these lakes are closed basins and situated within a rain‐shadow (∼300 mm precipitation per year) contributing to their hypersaline nature.…”

Section: Study Areamentioning

confidence: 99%

“…We targeted a series of hypersaline lakes located within the Cariboo Plateau of South-Central Interior British Columbia, Canada including Salt Lake, Last Chance Lake, and the Basque Lakes (Figure 1). The chemistry and geography of these lakes are described in greater detail in Nichols et al (2023). In short, these lakes are closed basins and situated within a rain-shadow (∼300 mm precipitation per year) contributing to their hypersaline nature.…”

Section: Study Areamentioning

confidence: 99%

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Leveraging Machine Learning Approaches to Predict Organic Carbon Abundance in Mars‐Analog Hypersaline Lake Sediments

Nichols,

Pontefract,

Masterson

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Modern advancements in laboratory and instrumental techniques in astrobiology have improved our life detection capabilities on both Earth and beyond. These advancements have also increased the complexity of data often resulting in data sets that are characterized by complex and non‐linear relationships. Machine learning methods are underutilized in astrobiology; however, these methods are extremely effective at revealing structure and patterns in complex data sets when paired with the right algorithms. Here, we employ a series of classification and regression algorithms to predict the abundance of organic carbon (OC) from X‐ray fluorescence (XRF) heavy element (>Mg) data in dynamic Mars‐analog hypersaline lake sediments. More specifically, we constructed models using the random forest, k‐nearest neighbors (KNN), support vector machine, and logistic regression algorithms. Overall, our trained models showed good performance with predicting the abundance of OC, with accuracies from 80% to 94%. Machine learning approaches such as classification and regression algorithms offer insight into complex data while providing agnostic insights, ultimately creating a more efficient search for OC. We applied our trained model on XRF data from Martian soil using rover‐based (PIXL) and orbital (Odyssey) data sets to produce probability predictions of OC abundance. Our predictions show a high probability that OC abundance is low which is comparable to OC data from recently landed missions. These results highlight the potential for predictive machine learning models to be trained on data from analog environments on Earth and then applied to extraterrestrial targets, ultimately, improving life detection efforts.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%