Developing Tailored Data Combination Strategies to Optimize the SuperCam Classification of Carbonate Phases on Mars
Bibliographic record
Abstract
Abstract The SuperCam instrument onboard the Mars 2020 Perseverance rover investigates Martian geological targets by a combination of multiple spectroscopic techniques. As Raman, Visible‐Infrared Spectroscopy, and Laser‐Induced Breakdown Spectroscopy (LIBS) spectra deliver complementary information about the interrogated sample, the multivariate analysis of combined spectroscopic data sets is here proposed as a tool to optimize the SuperCam capability to discriminate mineral phases on Mars. For this purpose, the laboratory study of carbonate phases within the Ca‐Mg‐Fe ternary system were selected as representative case of study. After the characterization of model samples, the discrimination capability of mono analytical Raman, VISIR, and LIBS data sets was evaluated by applying a chemometric approach based on the combination of principal component analysis (for sample clustering) and Linear Discriminant Analysis (for mineral classification). Afterward, the low‐level combination (LL) of Raman, VISIR, and LIBS data was achieved by concatenating their spectra into a single data matrix. The mineral classification achieved by LL data sets outperformed the mono analytical ones, thus proving the complementarity between molecular and elemental spectroscopic techniques. Mineral classification was further improved by using a mid‐level data combination strategy. After evaluating benefits and limitations afforded by the proposed combination strategies, future developments are finally outlined. As such, the final objective of this research line is to develop a classification model based on data combination to optimize the capability of SuperCam in discriminating relevant minerals on Mars, this being a key requirement for the selection of the optimal targets to be cached for the future Mars Sample Return Mission.
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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 | 0.001 |
| 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 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".