Framework for Optimizing Crop Selection for Future Space Missions
Bibliographic record
Abstract
Establishing sustainable food production systems is essential for supporting human life on long-duration space missions, where resupply opportunities are limited. Selecting the optimal crops for these systems is a critical task, as they must meet the crew’s nutritional demands within resource limitations. However, there is currently no overarching tool for selecting crops based on the mission-specific timeline, crew requirements, and available resources. This research addresses this gap by developing the framework for a dynamic crop selection model to enhance mission planning and increase the success of future lunar plant cultivation systems. The foundation of this framework includes the creation of a database for a selection of space system candidate crops. Key plant factors included in the database are the plants’ nutritional qualities and their growth requirements (e.g., temperature, humidity, water, nutrients, light, and growth cycle). The framework accounts for user inputs such as mission duration, the astronauts’ dietary needs, and the system’s constraints and environmental control capabilities. The projected output would include suggestions for the most suitable crops to be grown in the given bioregenerative life support system. This framework will lay the groundwork for a dynamic model. The interactivity of the model would allow for easy comparison of crop options and prediction of plant yield and resource consumption rates. Additionally, it will calculate oxygen production, carbon dioxide uptake, and water flows within the controlled environment, which is not only crucial to optimizing plant growth but to maintaining optimal air quality and astronaut health. This framework aims to ensure that the most appropriate crops are chosen for the mission scenario while optimizing the overall biomass production and nutritional output within the system. Being able to eventually simulate different biomass scenarios instantaneously will greatly improve the efficiency of lunar food production initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".