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>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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 it