Effects Of Noise Exposure On Hearing Health Evaluated Through Short Interval Otoacoustic Emission Monitoring
Bibliographic record
Abstract
Industrial workers are often exposed on a daily basis to noise doses that put them at risk of Noise-Induced Hearing Loss (NIHL), which remains the largest cause of indemnity in North America industries. To improve occupational hearing loss prevention programs, a method to continuously measure hearing fatigue using otoacoustic emissions (OAE) has already been proposed by the authors using a portable and robust OAE system designed for noisy industrial use. The effectiveness in the field of the aforementioned method is examined by comparing a control group of unexposed subjects in laboratory and noise exposed subjects in the field. All participants are equipped with an hearing protection earpiece which includes the usual OAE measurement components as well as a microphone mounted on the outer faceplate of the earpiece. At regular intervals over the course of the day, the growth function of distortion product OAEs is measured for both groups while simultaneously measuring noise levels inside and outside the ear. Preliminary results of this study are presented and analysed in the broader context of the dose-response relationship that could indicate individuals' risk to develop NIHL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".