Preparations for ExoMars: Learning Lessons from Curiosity
Bibliographic record
Abstract
In 2020, the European Space Agency will launch its first Mars rover mission, ExoMars. The rover will use a drill to obtain samples from up to 2m below the Martian surface that will then be analysed using a variety of analytical instruments, including the Raman Laser Spectrometer (RLS), which will be the first Raman spectrometer to be used on a planetary mission.To prepare for ExoMars RLS operations, we report on a series of experiments that have been performed in order to investigate the response of a representative Raman instrument to a number of analogue samples (selected based on the types of material known to be important, following investigations performed by NASA's Mars Science Laboratory, MSL, on the Curiosity rover). Raman spectroscopy will provide molecular and mineralogical information about the samples obtained from the drill cores on ExoMars. MSL acquires similar information using the CheMin XRD instrument which analyses samples acquired from drill holes several centimetres deep. Like Raman spectroscopy, XRD also provides information on the mineralogical makeup of the analysed samples.The samples in our study were selected based on CheMin data obtained from drill sites at Yellowknife Bay, one of the first locations visited by Curiosity (supplemented with additional fine scale elemental information obtained with the ChemCam LIBS laser instrument). Once selected (or produced), the samples were characterised using standard laboratory XRD and XRF instruments (in order to compare with the data obtained by CheMin) and a standard, laboratory based LIBS system (in order to compare with the ChemCam data). This characterisation provides confirmation that the analogue samples are representative of the materials likely to be encountered on Mars by the ExoMars rover.A representative, miniaturised Raman spectrometer was used to analyse the samples, using acquisition strategies and operating modes similar to those expected for the ExoMars instrument. The type of minerals detected are identified and compared to the information typically acquired using other analytical science techniques investigating in order to highlight the benefits and drawbacks of using Raman spectroscopy for planetary science applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".