Optimising human space exploration policies and strategies
Bibliographic record
Abstract
The key protagonists of human space exploration are pursuing different strategies. Yet in this international \nenvironment one would imagine cooperation rather than competition to be the most affordable. The long-term \nobjective of the US/NASA is to reach Mars and set up a sustained human presence. A Lunar Orbital PlatformGateway (LOP-G) would ultimately become a springboard to the Red Planet. This is consistent with a privatized/or \ndeactivated ISS starting in 2025, freeing NASA to concentrate on human space programmes beyond LEO. SpaceX \nwould create the capability of direct access to Mars, enabling regular shuttling from Earth. Asteroid mining retains \nsome commercial interest and would piggy back the above. The Chinese will continue to gain additional LEO \nexperience by establishing a larger space station, Tiangong-3, in the next decade - with the possibility of hosting \nEuropean astronauts. Later, China would deploy a permanent infrastructure on the Moon to explore and exploit local \nresources. Russia wished to continue its ISS/LEO programme as long as possible. Funding, a constant hurdle for the \nRussian space programmes, and the lack of reliable heavy lift capability remain challenging issues in their preparing \nfor human exploration beyond LEO. ESA plans are not yet formalised: the Aurora programme of Lunar/Mars \nexploration appears to be running out of strategic vision with ExoMars probably the culmination rather than the first \nstep; the Moon Village is still a concept and while it may materialise, the European lunar presence needs to be \nworked out. Since ESA will be without the independent means to put humans on the Moon for decades to come, its \nMember States are destined to fit into non-European strategies, seeking to capture specific niches, and more so to be \non the critical paths of major projects. Yet, it remains to be seen if such a demarche is acceptable by all, or any of the \nplayers. Japan and Canada, partners to the ISS, will have to find their place alongside other emerging space nations \nin the developing landscape of strategies laid out by US, China and ESA. After analysing these different strategies, \nthe paper will propose some scenarios based on a more holistic approach where the different players, including \nprivate entities, could contribute in a more synergistic mode, reducing costs, engaging throughout an improved path \nfor a sustainable human space exploration. The outcome of the 2nd ISEF will be taken into account in building the \ndifferent scenarios.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".