A hearing-care toolkit for young musicians: Combined use of a noise dose measurement app and acoustical manekin
Bibliographic record
Abstract
Within a joint initiative with the Schulich School of Music at McGill University, the authors developed an original approach to raise awareness about hearing health by combining a participative assessment with a novel noise exposure risk indicator. The overall noise exposure is estimated using two complementary measurement systems addressing, respectively, the contribution determined by (i) the use of portable music players and (ii) music and non-music activities. The first system is a “listening-level measuring kiosk,” a freely accessible station on which students can place their own headphones or earphones and playback (at their preferred listening levels) music excerpts from their own playlist on an acoustical manikin. The second system is a personal, smartphone-based, “noise exposure” portable measurement device. Users start with an initial training and an audiometry screening, followed by the collection of exposure data during a 4-week assessment period. Personalized results are provided based on individual exposure profile and initial audiogram. The validation of the design of a novel noise exposure risk indicator is based on the feedback from a focus group of young musicians. This paper describes the measurement systems, assessment process, and preliminary results from the ongoing pilot studies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".