MétaCan
Menu
Back to cohort
Record W2588827835 · doi:10.1515/opag-2017-0002

Agriculture for Space: People and Places Paving the Way

2017· article· en· W2588827835 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOpen Agriculture · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsLife support systemAgricultureAgency (philosophy)GeographyEnvironmental scienceEnvironmental protectionEcologyEngineeringArchaeologyBiologySociology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.244
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it