The journey to hearing protection fit-testing: from a doctoral project, to meeting a mentor, to a world recognized best practice
Bibliographic record
Abstract
Introduced in the mid-1970s, the concept of hearing protection device (HPD) fit-testing had been used by several researchers to assess on an individual wearer the exact amount of attenuation provided by a given HPD. In 2000, however, as part of his doctoral work, the author developed an objective method, referred to as Field-MIRE, involving simultaneous measurement with two microphones and dedicated to a new type of instantly molded custom earplugs. The so-called SonoPass measurement system was commercialized by Sonomax and first introduced during the NHCA conference held in Arizona in 2001. This was also for the author the opportunity to first meet Elliott H. Berger, one of his most cited author and soon to become mentor. Their formal collaboration started in 2006 when the F-MIRE system was adapted to test non-custom HPD, became exclusive to AEARO company, and was globally introduced as E-A-Rfit Validation System. Then years of further collaborative work led to ANSI S12.71 standard adopted in 2018 for field attenuation estimation systems (FAES). Along the way, their joint writing for the Noise Manual handbook was remembered by one as “one of the most daunting training job” and by the other as “a truly fun and amazingly rigorous exercise.”
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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 | 0.041 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".