Development of the Subjective Evaluation Method of Hearing Protectors
Bibliographic record
Abstract
Exposure to high sound levels causes hearing loss. Using hearing protection devices is one of the ways to prevent exposure to loud noises, reduce noise-induced hearing loss and prevent other problems such as cardiovascular disorders, blood pressure or noise annoyance. This study was conducted due to the prevalence increase of hearing loss in industries, despite the outspread in hearing protection programs. One of these reasons in increasing noise-induced hearing loss can be related to the inefficiency of hearing protectors evaluation methods. Hearing Protective tools are not evaluated at actual levels and therefore may perform differently when used in the field than in laboratory conditions. In this method, one step is completed by using the person's subjective response to the received sound before and after using the ear protector. This part is developed by defining the subjective perception of people and their feelings towards understanding the sound and scoring the answers and then converting it into decibel values of the sound. This research is somehow aimed at developing of subjective method for the measurement of sound attenuation based ISO standard 4869-1.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".