Bibliographic record
Abstract
Abstract Agricultural systems for space have been discussed since the works of Tsiolkovsky in the early 20 th century. Central to the concept is the use of photosynthetic organisms and light to generate oxygen and food. Research in the area started in 1950s and 60s through the works of Jack Myers and others, who studied algae for O 2 production and CO 2 removal for the US Air Force and the National Aeronautics and Space Administration (NASA). Studies on algal production and controlled environment agriculture were also carried out by Russian researchers in Krasnoyarsk, Siberia beginning in 1960s including tests with human crews whose air, water, and much of their food were provided by wheat and other crops. NASA initiated its Controlled Ecological Life Support Systems (CELSS) Program ca. 1980 with testing focused on controlled environment production of wheat, soybean, potato, lettuce, and sweetpotato. Findings from these studies were then used to conduct tests in a 20 m 2 , atmospherically closed chamber located at Kennedy Space Center. Related tests with humans and crops were conducted at NASA’s Johnson Space Center in the 1990s. About this same time, Japanese researchers developed a Controlled Ecological Experiment Facility (CEEF) in Aomori Prefecture to conduct closed system studies with plants, humans, animals, and waste recycling systems. CEEF had 150 m 2 of plant growth area, which provided a near-complete diet along with air and water regeneration for two humans and two goats. The European Space Agency MELiSSA Project began in the late 1980s and pursued ecological approaches for providing gas, water and materials recycling for space life support, and later expanded to include plant testing. A Canadian research team at the University of Guelph developed a research facility ca. 1994 for space crop research. The Canadian team eventually developed sophisticated canopy-scale hypobaric plant production chambers ca. 2000 for testing crops for space, and have since expanded their testing for a wide range of controlled environment agriculture topics. Most recently, a group at Beihang University in Beijing designed, built and tested a closed life support facility (Lunar Palace 1), which included a 69-m 2 agricultural module for air, water, and food production for three humans. As a result of these studies for space agriculture, novel technologies and findings have been produced; this includes the first use of light emitting diodes for growing crops, one of the first demonstrations of vertical agriculture, use of hydroponic approaches for subterranean crops like potato and sweetpotato, crop yields that surpassed reported record field yields, the ability to quantify volatile organic compound production (e.g., ethylene) from whole crop stands, innovative approaches for controlling water delivery, approaches for processing and recycling wastes back to crop production systems, and more. The theme of agriculture for space has contributed to, and benefited from terrestrial, controlled environment agriculture and will continue to do so into the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".