In Situ Optimized Substrate Witness Plates: Ground Truth for Key Processes on the Moon and Other Planets
Bibliographic record
Abstract
Abstract Future exploration efforts of the Moon, Mars, and other bodies are poised to focus heavily on persistent and sustainable survey and research efforts, especially given the recent interest in a long‐term sustainable human presence at the Moon. Key to these efforts understands a number of important processes on the lunar surface for both scientific and operational purposes. We discuss the potential value of in situ artificial substrate witness plates, powerful tools that can supplement familiar remote sensing and sample acquisition techniques and provide a sustainable way of monitoring processes in key locations on planetary surfaces while maintaining a low environmental footprint. These tools, which we call Biscuits, can use customized materials as wide ranging as zircon‐based spray coatings to metals potentially useable for surface structures, to target specific processes/questions as part of a small, passive witness plate that can be flexibly placed with respect to location and total time duration. We examine and discuss unique case studies to show how processes such as water presence/transport, presence and contamination of biologically relevant molecules, solar activity related effects, and other processes can be measured using Biscuits. Biscuits can yield key location sensitive, time integrated measurements on these processes to inform scientific understanding of the Moon and enable operational goals in lunar exploration. While we specifically demonstrate this on a simulated traverse and for selected examples, we stress all groups interested in planetary surfaces should consider these adaptable, low footprint, and highly informative tools for future exploration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".