Soilless cultivation of soybean for <scp>B</scp>ioregenerative <scp>L</scp>ife‐<scp>S</scp>upport <scp>S</scp>ystems: a literature review and the experience of the <scp>MEL</scp>i<scp>SSA P</scp>roject – <scp>F</scp>ood characterisation <scp>P</scp>hase <scp>I</scp>
Bibliographic record
Abstract
Higher plants play a key role in Bioregenerative Life-Support Systems (BLSS) for long-term missions in space, by regenerating air through photosynthetic CO2 absorption and O2 emission, recovering water through transpiration and recycling waste products through mineral nutrition. In addition, plants could provide fresh food to integrate into the crew diet and help to preserve astronauts' wellbeing. The ESA programme Micro-Ecological Life-Support System Alternative (MELiSSA) aims to conceive an artificial bioregenerative ecosystem for resources regeneration, based on both microorganisms and higher plants. Soybean [Glycine max (L.) Merr.] is one of the four candidate species studied for soilless (hydroponic) cultivation in MELiSSA, because of the high nutritional value of the seeds. Within the MELiSSA programme - Food characterisation Phase I, the aim of the research carried out on soybean at the University of Naples was to select the most suitable European cultivars for cultivation in BLSS. In this context, a concise review on the state-of-the-art of soybean cultivation in space-oriented experiments and a summary of research activity for the preliminary theoretical selection and subsequent agronomical evaluation of four cultivars will be presented in this paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".