The history of real-ear attenuation at threshold since 1957 with emphasis on the most recent ANSI S12.6-2016 and CSA Z94.2-2014 standards
Bibliographic record
Abstract
The American standard specifying the procedure for the measurement of real ear attenuation at threshold (REAT), often termed the gold standard in measuring hearing protector attenuation, was approved last year as an updated version, ANSI 12.6-2016. REAT was first standardized worldwide in the late 1950s in an American standard ANSI Z24.22-1957 and the method has evolved with time. Changes have affected the electroacoustic requirements for the sound field, instrumentation, audiometric method, and permissible background noise, but more importantly have also improved the specification of how the experimenter works with and fits the test subjects. So too, estimates of uncertainty are now included, and in the 2016 version they have been clarified and brought into harmony with ISO 4869 1. The ANSI standard also impacts Z94 since the latter standard references S12.6 for specification of the Canadian methodology. The author, who has been the chair since 1985 of the ANSI working group responsible for S12.6 and a member of the CSA working group responsible for Z94 since 1981, will compare and contrast the various methods and the Z94 requirements, and present representative data as well as a discussion of the expanded uncertainties that are specified in the most recent ANSI and ISO documents. Those values, for the 1/3 octave band test bands from 125 Hz to 8000 Hz, vary from approximately 1.5 2 dB for earmuffs and 2-3 dB for earplugs for within-laboratory testing, to 4-6 dB for earmuffs and 6.5-8 dB for earplugs for between laboratory measurements.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".