Earplug comfort: From subjective assessment on the field to objective measurement and simulation using augmented artificial heads
Bibliographic record
Abstract
For several years, lack of comfort has been pointed out as a major reason of earplug poor efficiency as noise control solution. The great complexity of comfort makes it difficult to predict it in the earplugs design phase. It is rather considered in an empirical way by the manufacturers using trial-and-errors approaches based on subjective assessment over a panel of subjects. Furthermore, because comfort is not quantified, Occupational Health and Safety (OHS) practitioners cannot select earplugs ensuring wearer comfort. To address these issues, a major international research project funded by two OHS institutes (IRSST in Canada and INRS in France) and involving several Universities (in Canada and England) started in 2017. The main objectives are to: (1) improve the understanding of earplugs comfort as perceived by field workers with consideration of all comfort components, (2) develop laboratory tools (augmented experimental and virtual artificial heads) to measure physical design variables related to the auditory, physiological, and functional components of comfort and (3) design a battery of hybrid objective/subjective comfort indices to quantify / measure / predict the different components of comfort. The aim of this presentation will be to present the project and first results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".