Hearing protector labeling for active noise reduction devices
Bibliographic record
Abstract
The US EPA regulation 40 CFR part 211b does not specify how to label hearing protectors that use active noise reduction (ANR). Real-ear attenuation at threshold (REAT) measurements are appropriate for testing the passive noise reduction performance of the earmuff. The contribution of ANR to the overall attenuation must be measured either with an acoustic test fixture (ATF) or using the microphone in real ear (MIRE) technique. ATFs must adequately mimic the coupling of the protector with either the skin of the ear canal or the side of the head, as well as provide sufficient attenuation to eliminate effects of bone conduction. The MIRE technique uses a miniature microphone positioned in the ear canal either at the entrance or at the tympanic membrane, and can be used to measure the insertion loss between the occluded and unoccluded conditions for both the passive and active modes of the protector. ATF and MIRE measurements made for several protectors demonstrate effective performance below 1000 Hz. This paper will examine methods for combining ATF and MIRE measurements with REAT to develop effective and informative rating for the ANR class of hearing protectors. [Portions of work supported by the U.S. EPA IA 75090527.]
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.027 |
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".