Bibliographic record
Abstract
Space is the next border that humanity needs to cross to reach new developments. Yet, space exploration faces numerous challenges, especially when it comes to hazard putting in danger human health. While a lot of efforts are being made to mitigate the impact of space travel on physical health, mental health of space travelers is also highly at risk, notably due to isolation and the associated lack of meaningful social interactions. Given the social potentiality of artificial agents, we propose here that social robots could play the role of social partners to mitigate the impact of space travel on mental health. We will explore the logics behind using robots as partners for in-space social training. We will then identify what are the advantages of using social robots for this purpose, either for crew members and passengers on shorter spaceflights, or for potential colons for possible future longer-term space exploration missions. • Space exploration faces numerous challenges. • Space travel negatively impacts human health. • Mental health of space travelers is at risk due to social isolation. • Social robots could mitigate the impact of space travel on mental health. • Robots could act as partners for in-space social training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".