Bibliographic record
Abstract
To quickly estimate the amount of attenuation provided by any given hearing protection device (HPD) on any given individual, a smartphone app has been developed. The app features an audio stimulus generator to generate loud tones over the smartphone (or tablet) embedded loudspeakers, a graphical user interface where the user can report the count of audio stimuli perceived, and an attenuation prediction algorithm able to estimate the overall attenuation of the HPD under test, only knowing its type. The proposed approach relies on a supra-threshold method where sequence of 1 kHz-centered narrowband stimuli are played in decreasing levels with steps of 5 dB. The user simply counts the number of tonal bursts perceived before stimuli become inaudible in two conditions: with open ears and with both ears occluded with the HPD. The two counts values are entered within the app to compute an estimate of the overall HPD attenuation. This estimate is computed using the “octave band method” as an C-A overall attenuation, that represents the average difference between the C-weighted overall sound pressure level and the A-weighted overall sound pressure level attenuated by the HPD. The measured attenuation values using REAT and using the tablet are compared for two types of earplugs: a roll-down foamplug and premoded earplug. They show that while the REAT values obtained at 1 kHz are poorly predicted, the overall REAT attenuation values are better predicted and that a polynomial regression model could possibly be built to predict with reasonable accuracy the overall attenuation of foamplugs -and many other types of HPDs- using only a tablet or smartphone generating 1 kHz tonal bursts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".