LIBS-Raman Multimodal Architecture for Automated Lunar Prospecting
Bibliographic record
Abstract
A fundamental aspect of contemporary space programs revolves around optimizing the use of lunar in situ resources, known as In Situ Resources Utilization (ISRU). This strategy has the potential to significantly cut down the immense energy requirements for human space exploration and, equally important, reduce the costs associated with launching satellites into orbit. However, the Moon is largely unexplored from a resource standpoint and needs high-resolution instruments to assess the resource concentration as well as the nature of a sample. Given the vastness of the lunar (sub)surface exploration area and its complexity, a diverse array of instruments is required to establish an efficient and autonomous system for characterizing lunar regolith. This paper aims to propose a multimodal machine learning model developed to identify minerals using Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) instruments based on a multimodal fusion architecture.
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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) | 0.000 | 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".