Expanding frontiers: harnessing plant biology for space exploration and planetary sustainability
Bibliographic record
Abstract
Plants are critical for sustaining human life and planetary health. However, their potential to enable humans to survive and thrive beyond Earth remains unrealized. This Viewpoint presents a collective vision outlining priorities associated with plant science to support a new frontier of human existence. These priorities are drawn from the International Space Life Sciences Working Group (ISLSWG) Plants for Space Exploration and Earth Applications workshop, held at the European Low Gravity Research Association (ELGRA) conference in September 2024. First, we highlight transformative advances gained from using the 'laboratory of space' in understanding how plants respond to gravity and other stressors. Second, we introduce a new crop Bioregenerative Life Support System (BLSS) readiness level (BRL) framework - extending the existing Crop Readiness Level (CRL) - to assist in overcoming challenges to establish resilient, sustainable crop production. Materializing the vision of plants as enablers of space exploration will require innovative approaches, including predictive modeling, synthetic biology, robust Earth-based analogue systems, and reliable space-based instruments to monitor biological processes. Success relies upon a unified international community to promote sharing of resources, facilities, expertise, and data to accelerate progress. Ultimately, this work will both advance human space exploration and provide solutions to enhance sustainable plant production on Earth.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".