Bibliographic record
Abstract
INTRODUCTION: Research on the mortality of space explorers has focused exclusively on U.S. astronauts and Soviet and Russian cosmonauts. However, other nations have organized space programs over the last 40 yr and the European Space Agency, the Canadian Space Agency, the China National Space Administration, and the Japan Aerospace Exploration Agency all offer an opportunity for further study of the mortality of space explorers.METHODS: We used biographical and vital data abstracted from public sources for European, Canadian, Chinese, and Japanese astronauts. Using general population mortality rates from the Human Mortality Database and mortality rates derived from the cohort of U.S. astronauts, we computed standardized mortality ratios.RESULTS: The groups displayed different preferences in selection of astronauts. As there were no deaths in any of the four groups, the point estimates for standardized mortality ratios were all 0. However, the European cohort experienced a statistically significant reduction in all-cause mortality risk in comparison to the European general population as well as in comparison to U.S. astronauts.DISCUSSION: The healthy worker effect predicts that all study cohorts should have lower all-cause mortality risk in comparison to their general populations. The general population of Japan has mortality rates low enough that any reduction in mortality risk may remain undetectable in the Japanese cohort. Continued surveillance of these populations in the coming decades will make them a useful addition to the evidence base for astronaut mortality.Reynolds RJ, Day SM. Mortality among international astronauts. Aerosp Med Hum Perform. 2019; 90(7):647–651.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".