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Record W2153926936 · doi:10.2514/6.2011-5198

Expedition Mars: A Mars Analogue Program Dedicated to Advancing Competency in Human Planetary Surface Exploration

2011· article· en· W2153926936 on OpenAlexaffabout
Lealem Mulugeta, M. Battler, Rocky Persaud, Ryan L. Kobrick, John Thaler, Randall Shelaga

Bibliographic record

Venue41st International Conference on Environmental Systems · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsMars Exploration ProgramAstrobiologyExploration of MarsMars landingPlanetary explorationEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Mars analogue research facilities are excellent tools for advancing comparative planetology scientific research and exploration methodologies, as well as for investigating and devising countermeasures for the challenges astronauts may encounter while exploring the Martian surface. Analogue studies, and more specifically human mission simulations, also play an important role by training the future leaders in space exploration. With this in mind, the Mars Society of Canada (MSC) established the Expedition Mars program to advance the competence (knowledge, expertise and leadership) needed for human exploration of Mars. The program is divided into two distinct, but complementary, Mars analogue expedition series: Expedition Mars Analogue Training Series (ExMATS) and Expedition Mars Analogue Research Series (ExMARS). The ExMATS missions are designed to train and certify researchers, engineers and commanding officers for ExMARS missions which are dedicated to conducting extended Mars analogue research missions for maximum return on scientific research. Through the two series, Expedition Mars has established a systematic and sustainable method of developing and transferring competence within the program, and to the space community.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.806

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.060
GPT teacher head0.259
Teacher spread0.199 · 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 designObservational
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
Published2011
Admission routes2
Has abstractyes

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