Improving Exposure Estimates by Combining Exposure Information
Bibliographic record
Abstract
OBJECTIVES: Any exposure estimation technique has inherent strengths and limitations. In an effort to improve exposure estimates, this study developed and evaluated the performance of several hybrid exposure estimates created by combining information from individual assessment techniques. METHODS: Construction workers (n = 68) each completed three full-shift noise measurements over 4 months. Three single exposure assessment techniques [trade mean (TM), task-based (TB), and subjective rating (SR)] were used to estimate exposures for each subject. Hybrid techniques were then developed which incorporated the TM, SR, and TB noise exposure estimates via arithmetic mean combination, linear regression combination, and modification of TM and TB estimates using SR information. Exposure estimates from the single and hybrid techniques were compared to subjects' measured exposures to evaluate accuracy. RESULTS: Hybrid estimates generally were more accurate than estimates from single techniques. The best-performing hybrid techniques combined TB and SR estimates and resulted in improvements in estimated exposures compared to single techniques. Hybrid estimates were not improved by the inclusion of TM information in this study. CONCLUSIONS: Hybrid noise exposure estimates performed better than individual estimates, and in this study, combination of TB and SR estimates using linear regression performed best. The application of hybrid approaches in other contexts will depend upon the exposure of interest and the nature of the individual exposure estimates available.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".