Statistical assessment behind a standard on hearing protector field attenuation measurement devices.
Bibliographic record
Abstract
New measurement systems for assessing individual hearing protection device (HPD) performance in the field have been developed over the past several years to address the question of what amount of protection is a given individual getting from an HPD. Although these systems, referred to as field attenuation measurement systems (FAMSs), have the same purpose and produce attenuation values that are presented in similar ways, the underlying technology used to produce a personal attenuation rating (PAR) can be drastically different, ranging from psychophysical tests to objective microphone measurements and involving single or multiple frequency measurements. In an effort to ensure that FAMS provide an attenuation rating that is a both a scientifically valid number and a meaningful reading for the end-user, the members of the American National Standard Institute Working Group 11 have recently been starting to work on a proposed standard to specify the minimum performance criteria for a FAMS to assure it provides data with defined accuracy and precision. The current paper describes the underlying statistical assessments that are considered in the standard. Specifically, the computational details of the number of subjects required for various assessments made within the standard for the determination of the maximum permissible background noise, measurement uncertainty, and HPD fit uncertainty will be presented. The paper will also detail the calculation of the repeatability and reproducibility.
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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 | 0.272 | 0.465 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".