Bibliographic record
Abstract
The EDEN ISS nutrient delivery system (NDS) is based on proven horticultural management technology. While some system aspects are relatively new, they will not impact the overall reliability of operation. Similarly, the control system that will operate the NDS and other subsystems is based on Argus Controls (Surrey, BC, Canada) hardware, which has been a greenhouse control and environment management standard since 1984. The bulk of the NDS hardware is contained within the FEG, a 6 meter insulated shipping container equipped with eight growing racks holding a total of forty-two plant growth trays covering 11.9 square metres of growing space. The system was designed to grow lettuce, herbs, pepper, tomato and cucumber and will supply the Neumayer Station III crew with fresh produce during operation. Plants will be grown in recirculating nutrient solution that has both pH and electrical conductivity (EC) control. Water will be delivered to the modular growing trays using a hybrid aeroponic/nutrient film technique (NFT) from two nutrient tanks located in the service section. While the NDS in itself uses reliable and proven hardware and growing techniques, the challenges of remote deployment in the Antarctic will provide the needed operational and productivity data required for future missions to the Moon or Mars
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.374 | 0.319 |
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".