Determine the Elemental Composition of Minerals From Complex Solid‐Solution Series by Raman Spectroscopy: Implications for Mars Exploration Missions
Bibliographic record
Abstract
ABSTRACT Garnets are minerals offering valuable insights into geological and planetary processes, in both terrestrial and extraterrestrial contexts. Their presence in metamorphic rocks, as well as in meteorites, makes them key indicators of the conditions under which they formed, such as pressure, temperature, and chemical environment. This information is valuable for understanding the history and evolution of planetary bodies, including Earth and Mars. Developing automatic classification models for garnets using Raman spectroscopy has a potential application for planetary exploration, particularly for the Raman Laser Spectrometer (RLS) on the Rosalind Franklin rover of the upcoming ExoMars mission. In this work, we used a dataset combining spectra from ADAMM and RRUFF databases, ensuring a well‐distributed representation of the two main garnet groups: pyralspites and ugrandites. The spectra from ADAMM were obtained using the RLS SIM, a laboratory version of RLS, while RRUFF data helped increase the number of samples in the dataset. After standardizing all data, we designed a two‐step classification model: a top‐level model to classify into the two main groups and two specific models to classify the garnet type within each group. We tested multiple machine learning algorithms, including support vector machine (SVM), k‐nearest neighbors (KNN), artificial neural networks (ANN), decision tree, and naive Bayes. The top‐level classification reached 100% accuracy in testing, while the final combined model achieved 80.42% accuracy. These results demonstrate that machine learning and Raman spectroscopy can effectively classify garnets, providing a valuable tool for planetary missions and mineralogical studies.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".