Space Analogs and Behavioral Health Performance Research review and recommendations checklist from ESA Topical Team
Bibliographic record
Abstract
Space analog research has increased over the last few years with new analogs appearing every year. Research in this field is very important for future real mission planning, selection and training of astronauts. Analog environments offer specific characteristics that resemble to some extent the environment of a real space mission. These analog environments are especially interesting from the psychological point of view since they allow the investigation of mental and social variables in very similar conditions to those occurring during real space missions. Analog missions also represent an opportunity to test operational work and obtain information on which combination of processes and team dynamics are most optimal for completing specific aspects of the mission. A group of experts from a European Space Agency (ESA) funded topical team reviews the current situation of topic, potentialities, gaps, and recommendations for appropriate research. This review covers the different domains in space analog research including classification, main areas of behavioral health performance research in these environments and operational aspects. We also include at the end, a section with a list or tool of recommendations in the form of a checklist for the scientific community interested in doing research in this field. This checklist can be useful to maintain optimal standards of methodological and scientific quality, in addition to identifying topics and areas of special interest.
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 | 0.024 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".