Measurements of the neutron dose and energy spectrum on the International Space Station during expeditions ISS-16 to ISS-21
Bibliographic record
Abstract
As part of the international Matroshka-R and Radi-N experiments, bubble detectors have been used on board the ISS in order to characterise the neutron dose and the energy spectrum of neutrons. Experiments using bubble dosemeters inside a tissue-equivalent phantom were performed during the ISS-16, ISS-18 and ISS-19 expeditions. During the ISS-20 and ISS-21 missions, the bubble dosemeters were supplemented by a bubble-detector spectrometer, a set of six detectors that was used to determine the neutron energy spectrum at various locations inside the ISS. The temperature-compensated spectrometer set used is the first to be developed specifically for space applications and its development is described in this paper. Results of the dose measurements indicate that the dose received at two different depths inside the phantom is not significantly different, suggesting that bubble detectors worn by a person provide an accurate reading of the dose received inside the body. The energy spectra measured using the spectrometer are in good agreement with previous measurements and do not show a strong dependence on the precise location inside the station. To aid the understanding of the bubble-detector response to charged particles in the space environment, calculations have been performed using a Monte-Carlo code, together with data collected on the ISS. These calculations indicate that charged particles contribute <2% to the bubble count on the ISS, and can therefore be considered as negligible for bubble-detector measurements in space.
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 | 0.001 |
| 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) | 0.001 | 0.000 |
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".