Bibliographic record
Abstract
Text: When the first images of the planet Earth were received, many of us discovered our beautiful blue planet, not the darkness of outer space. Based on Tsiolkovsky's discoveries on the rocket principles, Werner von Braun built the Saturn rocket family to launch heavy payloads to Earth orbit and beyond, and the Gemini program helped to design the safe transportation of humans to space. Nowadays, a lot of new science and technology are helping humans to head again to the Moon. NASA, the European Space Agency, China, India, Japan, and including Canada, have announced plans to send humans to the Moon, or are studying how to go. The European Space Agency has been developing a prototype of an oxygen plant to produce oxygen from lunar regolith, a material found on the surface of the Moon and containing between 40-45 percent oxygen. This oxygen plant could help astronauts reduce cargo since it could make breathable air and rocket fuel as well. NASA is developing an orbital space station near the Moon, named the Gateway. The idea is to use the Gateway to transfer vehicles, reusable lunar landers carrying the crew from the lunar surface to and from low lunar orbit. The ambition of NASA is high, and they described it as an open architecture to foster new capabilities to explore the Moon. From new missions to Mars, mining asteroids, fundamental research, and the recent discovery of a sunlike star TYC 8998-760-1 accompanied by two giant exoplanets, human space exploration helps to address fundamental questions about our place in the Universe and ourselves. The challenges we face related to space exploration helps us to leverage technology, create new industries, and contribute to global cooperation between all nations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.096 | 0.046 |
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".