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Record W1598378328 · doi:10.4271/2007-01-3065

Martian Gardens: Student Designs of Bio-Regenerative Life Support Systems in a 1st Year Undergraduate Seminar Course

2007· article· en· W1598378328 on OpenAlexaffabout
Geoffrey Waters, Michael Dixon

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCourse (navigation)MartianAstrobiologyLife support systemComputer scienceEngineeringMars Exploration ProgramAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

The following abstract is that provided to first year undergraduate students as part of the recruitment effort for 1st Year Seminar Courses at the University of Guelph. When humankind begins the colonization of the moon or Mars, we will be bringing along more of Earth than one might think. A number of space and government agencies around the world, including researchers at the Controlled Environment Systems Research Facility, University of Guelph, are involved in the design and engineering of self-contained ecosystems based on Earthly biological processes. These processes can be harnessed, with complementary physical and chemical technologies to support human life (food production, air revitalization, psychology) in the hostile conditions of space. Using a problem based learning approach, students registered in this seminar course will study a variety of international projects devoted life support systems development including those of the Canadian Space Agency, European Space Agency and NASA. Students will rely on each other3s interdisciplinary background to design a Martian habitat and life support system that is sustainable, while meeting the challenges of keeping crew happy in a sealed environment. The political issues associated with human space exploration and the ethical questions it arises will be tackled using a variety of group discussions, presentations and debates. The course instructors highly encourage enrolment from students with backgrounds and interest in a variety of disciplines including the life and physical sciences, the social sciences and engineering.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.122
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1220.021

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2007
Admission routes2
Has abstractyes

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