Intra-subject fit variability using field microphone-in-real-ear attenuation measurement for foam, pre-molded and custom molded earplugs
Bibliographic record
Abstract
In recent years, the arrival of several field attenuation estimation systems (FAES) on the industrial marketplace have enabled better assessment of hearing protection in real-life noise environments. FAES measure the individual attenuation of a given hearing protection device (HPD) as fitted by the end-user, but FAES enable predictions based only on measurements taken over a few minutes and do not account for what may occur later in the field over months or years as the earplug may be fitted slightly differently over time. This paper will use the field microphone-in-real-ear (F-MIRE) measurement technique to study in the laboratory how consistently a subject can fit and refit an HPD. A new metric, the intra-subject fit variability, will be introduced and quantified for three different earplugs (roll-down foam, premolded and custom molded), as fitted by two types of test subjects (experienced and inexperienced). This paper will present the experimental process used and statistical calculations performed to quantify intra-subject fit variability. As well, data collected from two different laboratories will be contrasted and reviewed as to the impact of trained versus untrained test subjects.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".