ExoMars Raman Laser Spectrometer: A Tool to Semiquantify the Serpentinization Degree of Olivine-Rich Rocks on Mars
Bibliographic record
Abstract
We evaluated the effectiveness of the ExoMars Raman laser spectrometer (RLS) to determine the degree of serpentinization of olivine-rich units on Mars. We selected terrestrial analogs of martian ultramafic rocks from the Leka Ophiolite Complex (LOC) and analyzed them with both laboratory and flight-like analytical instruments. We first studied the mineralogical composition of the samples (mostly olivine and serpentine) with state-of-the-art diffractometric (X-ray diffractometry [XRD]) and spectroscopic (Raman, near-infrared spectroscopy [NIR]) laboratory systems. We compared these results with those obtained using our RLS ExoMars Simulator. Our work shows that the RLS ExoMars Simulator successfully identified all major phases. Moreover, when emulating the automatic operating mode of the flight instrument, the RLS ExoMars Simulator also detected several minor compounds (pyroxene and brucite), some of which were not observed by NIR and XRD ( e.g ., calcite). Thereafter, we produced RLS-dedicated calibration curves ( R 2 between 0.9993 and 0.9995 with an uncertainty between ±3.0% and ±5.2% with a confidence interval of 95%) to estimate the relative content of olivine and serpentine in the samples. Our results show that RLS can be very effective in identifying serpentine, a scientific target of primary importance for the potential detection of biosignatures on Mars—the main objective of the ExoMars rover mission.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".