<i>In-situ</i> personal assessment of hearing protector performance–recommendations for an updated standard
Bibliographic record
Abstract
Standardized method currently used to assess Hearing Protection Devices (HPD) performance have little correspondence with protection achieved by users in practice. To overcome this problem new measurement method are being proposed. They no longer rely on statistically-based, population-based estimate, such as the Noise Reduction Rating (NRR), but on an individual assessment of the performance of the HPD. These new methods are highly beneficial for effective Hearing Conservation Programs as they provide individual attenuation values for noise-exposed workers in lieu of the statistically-based laboratory performance estimate, but their implementation will necessitate complete revision of existing standards. Among the issues to be addressed, are the aspect of the accuracy of the measurements performed by those methods and associated devices and also the replacement of any single number rating of HPD (such as the NRR) in existing standards. Solutions to address theses issues and some general guidelines for an updated standard will be presented.
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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 | 0.048 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.013 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".