Assessment of otoacoustic emission probe fit at the workfloor
Bibliographic record
Abstract
In the workplace, practices in occupational health to prevent noise-induced hearing loss (NIHL) are currently based on a group average of exposure/damage relationships.These practices do not take into account the individual susceptibility to NIHL which is an important factor in a worker's actual risk of hearing loss.To evaluate and improve the effectiveness of personal hearing protection at the workfloor, an in-field measurement procedure of otoacoustic emissions (OAE) has been developed and validated.Unsupervised evaluation of OAE probe placement during the work shift is an important challenge for in-field OAE measurement.In this regard, proper OAE probe fit in the ear canal is a major concern in order to provide optimal passive noise attenuation to ensure that the worker's hearing is protected and improve signal-to-noise ratio of OAE measurements.In the following study, a lumped elements model of an occluded ear canal is used; first, to analyze the effects of probe fit leakage on the loudspeaker transfer function.Second, to validate the proposed method by comparing the model's transfer functions with those estimated during experiments with an OAE probe and tube setup.Afterwards, the probe's passive noise attenuation is calculated for different leaks by measuring sound pressure level inside and outside the occluded tube.Finally, the relationship between the probe's passive attenuation, miniature loudspeaker response and leakage is established.This proposed approach could assess the probe fit in situ and solve problems of unsupervised evaluation of probe placement by automatically warning the wearer of an improper fit after the loudspeaker response measurement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".