Lunar Agricultural Module Ground Test Demonstrator – an International Approach for Realizing Plant-Based Bio-Regenerative Life-Support
Bibliographic record
Abstract
Long-term human lunar exploration requires advancement of life support systems. Bio-regenerative life support systems (BLSS) have been shown to have a reduced equivalent mass for such long-term missions compared to more traditional methods, e.g., physical-chemical life support, which reduces mission costs and effort in general. Currently, no such (near) closed-loop systems exist and several challenges for their realization are still present, such as understanding of e.g., scaling of the system, interaction of technical and biological components, interaction and dependency of technical elements, and the microbiome. Beginning with a prototype-test in Antarctica, the German Aerospace Center (DLR) has joined an international effort, comprising the Canadian Space Agency (CSA) and several subcontractors, in now creating a Ground Test Demonstrator (GTD) capable of addressing the many unknowns of designing, developing and operating a Lunar Agriculture Module (LAM). This paper aims to present the current updated status of the design and project and informs about the international effort behind it, highlighting the importance of the project. We present a system overview and inform about how the system will be able to address open issues currently associated with BLSS as well as a roadmap of the LAM-GTD usage and how it will eventually lead to the operation of an actual agricultural module on the lunar surface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".