Assessing the feasibility of laser induced breakdown spectroscopy for detecting nitrogen in martian surface sediments
Bibliographic record
Abstract
Despite the detection of fixed nitrogen in meteorites and directly on Mars' surface, the abundance and distribution of nitrogen sequestered in the martian crust remains unknown. Given that nitrogen is a bioessential element that is required for the synthesis of amino acids , nucleic acids , and other organic molecules vital for life, this gap in knowledge is one of the most important challenges in constraining martian habitability . Laser-induced breakdown spectroscopy (LIBS) has the capability to detect N in natural rock samples and is available as a stand-off survey instrument on multiple currently active Mars rovers, creating an immediate opportunity to map the stratigraphic distribution of N within diverse depositional settings. However, little has been published regarding the detection of N with LIBS. To lay a foundation for N detection on Mars using LIBS, we synthesized a comprehensive suite of samples with variable amounts of nitrogen (as nitrate or ammonium) in either a Mars regolith simulant or a clay matrix. We present baseline spectra of N emission in Mars-relevant matrices and identify spectral interferences. Our results indicate that 17 diagnostic N emission lines are reliably detectable from mineral-bound N against a basaltic background, but only four lines exhibit sufficient sensitivity to be detected across a range of N concentrations and within all tested matrices. To elucidate optimized strategies for quantification, we present an iterative series of PLS models. We find that prediction accuracy is improved by restricting the compositional range of the training set, normalizing the data, subtracting baseline continuum emission, and simultaneously modeling the emission behaviour of multiple diagnostic N lines at once. We observe that the prediction uncertainty increases (worsens) from 8.4% to 29.9% if models are used to predict N in samples with a dissimilar matrix than those used during training, suggesting poor generalizability outside the training range. Consequently, future work should focus on developing a larger, more diverse training set that encompasses the range of N concentrations and phases expected to be encountered on Mars, which may be used to train generalizable models. Overall, this work demonstrates that LIBS is a promising tool for determining the abundance of N sequestered in martian surface materials and lays a foundation for future development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".