Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".