Improving the accuracy of noise exposure estimates for workers with highly variable exposures.
Bibliographic record
Abstract
Noise exposure assessment is difficult, and particularly so for workers with variable noise levels. We evaluated exposure estimates created using three different techniques: trade-mean, task-based, and subjective rating. We created trade-mean, task-based, and subjective rating estimates for a group of 68 construction workers using information collected on three workshifts over 4 months. This information included their trade, the tasks performed on each workshift, their subjective ratings of their noise exposures, as well as a full-shift exposure measurement on each of the three workshifts. We then created hybrid exposure assessment techniques using various combinations of the trade-mean, task-based, and subjective rating estimates, and compared these hybrid estimates to subjects’ measured exposures and to estimates from the single techniques. Hybrid techniques generally resulted in substantial improvements in accuracy compared to the single techniques. A linear regression-based hybrid approach had the best performance, but two much simpler hybrid techniques did nearly as well. Adding trade-mean information did not improve the accuracy of hybrid estimates. These results suggest that a hybrid regression technique combining task-based and subjective rating estimates may produce the most accurate estimates of exposure for workers with highly variable noise exposures.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".