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Record W2284433591

Lessons Learnt About Public Interest in Analogue Test Site Missions

2014· article· en· W2284433591 on OpenAlexfundno aff
Volker Maiwald

Bibliographic record

Venueelib (German Aerospace Center) · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAmes Research CenterEötvös Loránd TudományegyetemDeutsches Zentrum für Luft- und RaumfahrtUniversiteit LeidenGeorge Washington UniversityNational Aeronautics and Space Administration
KeywordsMars Exploration ProgramAeronauticsExploration of MarsCrewTest (biology)EngineeringAerospaceComputer scienceOperations researchAstrobiologyAerospace engineeringGeology
DOInot available

Abstract

fetched live from OpenAlex

During early 2013 the German Aerospace Center (DLR) participated in a simulated Mars mission at the Mars Desert Research Station (MDRS) in Utah, USA. The author has been a member of Crew 125, also known as the International Lunar Exploration Working Group’s (ILEWG) EuroMoonMars B mission. While launching with a small article on DLR’s website, the press attention for this mission grew significantly in the weeks after the simulated trip to Mars. In this paper the media coverage of this research stay at MDRS is described and it is investigated how the apparent interest of the public in human spaceflight missions can and should be increased by improving awareness of analogue test site operations and missions and increasing the coverage of a human and emotional side rather than an exclusively technical point of view for such missions. The costs involved in EuroMoonMars B are related to the media outcome to show that for a humble investment, public interest can be triggered. The author describes further how enhancing analogue test site utilization helps increase public support and also is a legit way of justifying spendings on human missions to other planets. It is also suggested to involve the public more in comparable analogue missions to increase the understanding and support for human exploration efforts.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.788

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 communication0.0000.000
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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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
Published2014
Admission routes1
Has abstractyes

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