ExoMars Raman Laser Spectrometer (RLS): development of chemometric tools to classify ultramafic igneous rocks on Mars
Bibliographic record
Abstract
This work aims to evaluate whether the multi-point analysis the ExoMars Raman Laser Spectrometer (RLS) will perform on powdered samples could serve to classify ultramafic rocks on Mars. To do so, the RLS ExoMars Simulator was used to study terrestrial analogues of Martian peridotites and pyroxenites by applying the operational constraints of the Raman spectrometer onboard the Rosalind Franklin rover. Besides qualitative analysis, RLS-dedicated calibration curves have been built to estimate the relative content of olivine and pyroxenes in the samples. These semi-quantitative results, combined with a rough estimate of the concentration ratio between clino- and ortho-pyroxene mineral phases, were used to classify the terrestrial analogues. XRD data were finally employed as reference to validate Raman results. As this preliminary work suggests, ultramafic rocks on Mars could be effectively classified through the chemometric analysis of RLS data sets. After optimization, the proposed chemometric tools could be applied to the study of the volcanic geological areas detected at the ExoMars landing site (Oxia Planum), whose mineralogical composition and geological evolution have not been fully understood.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".