Influence of impulse noise on noise dosimetry measurements on the International Space Station
Bibliographic record
Abstract
Objective: Acoustic dosimetry (AD) data collected on the International Space Station (ISS) were analysed to investigate the impact of impulse noise on crew noise exposure.Design: The noise exposure during work (LAeq16h) and sleep (LAeq8h) time, and the number of impulses >115 dB peak that occurred during each measurement activity, were calculated from the AD data. Two parametric studies were used to estimate the effect of 1) impulses in the original data set, and 2) hypothetical impulses of different levels, durations and quantities on LAeq16h.Study sample: Twelve sets of AD data collected on the ISS from November 2017 to October 2018.Results: The ISS work time noise limit (72 dBA) was exceeded in four of the 12 data sets. In three of those, there were over 100 impulses >115 dB peak and the number of impulses was significantly correlated with LAeq16h. However, the impulses only caused a meaningful increase in LAeq16h when the number of occurrences was large (>50), or when both the level and duration of the impulses were large.Conclusions: Continued monitoring of impulse noise data is recommended to facilitate the investigation of exceedances or abnormalities in future AD data acquired on the ISS.
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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 | 0.004 |
| 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".