Gravitational and Space Biology 23(2) August 2010 3 MELiSSA: THE EUROPEAN PROJECT OF CLOSED LIFE SUPPORT SYSTEM
Bibliographic record
Abstract
The MELiSSA (Micro-ecological life-support system) project is intended to be a tool to gain understanding of closed life-support systems, and consequently a knowledge base for European development of regenerative life-support systems for long-term manned missions (e.g. lunar base, Mars mission). The driving elements of MELiSSA are the production of food, water, and oxygen from the organic wastes of the mission (e.g., urine, CO2,). Inspired by a terrestrial “aquatic ” ecosystem, the MELiSSA process consists of five main sub-processes called compartments, from the anoxygenic thermophilic up to the photo-autrophic (e.g., higher plants). The choice of this compartmentalized structure is required by the very high level of space requirements in terms of robustness and safety. During the 20 years of the project, a very progressive and structured approach has been developed to characterize, model, and control the MELiSSA loop. This approach starts from the selection of the involved sub-processes, up to its predictive control. The project is structured on a Memorandum of Understanding (MOU) and is managed by ESA. It involves roughly 30 organizations encompassing Europe and Canada; eleven of these organizations, called partners, have
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".