MétaCan
Menu
Back to cohort
Record W4416919979 · doi:10.32865/2346/102750

Canadian Food Production Subsystem Designs for a Lunar Agriculture Module - Ground Test Demonstrator (LAM-GTD)

2025· article· en· W4416919979 on OpenAlexaboutno aff
Jared Stoochnoff, Conrad Zeidler, Michel Fabien Franke, Vincent Vrakking, Volker Maiwald, Daniel Schubert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureCrewSustainable agricultureFood processingAgency (philosophy)Life support systemPrecision agricultureKey (lock)

Abstract

fetched live from OpenAlex

The ability to produce fresh food in situ will be a beneficial addition to the future of sustainable lunar surface exploration, reducing reliance on traditional physiochemical life support systems and pre-packaged food supplies from Earth. To advance Controlled Environment Agriculture technologies and crew operations in preparation for a Lunar Agriculture Module, the Canadian Space Agency and the German Aerospace Center have conceptualized and progressed the design of a Lunar Agriculture Module - Ground Test Demonstrator. The purpose of this high-fidelity demonstrator would be to increase the technology readiness levels of all major greenhouse subsystems, simulate crew operations, and inform engineering requirements for an eventual lunar surface design. Canada will contribute to the design of several key subsystems, including the Nutrient Delivery System, the Light Control System, the Versatile Assistant robotic arm, a Plant Health Monitoring System, and technologies that could improve confidence in the module's food safety processes. This paper outlines the progress made to date on these key subsystems and how our teams intend to use the Lunar Agriculture Module - Ground Test Demonstrator to prepare for the challenge of a Lunar Agricultural Module.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.201
Teacher spread0.183 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicLight effects on plantsFrench-language works237,207