Evaluation and Comparison of Three Exposure Assessment Techniques
Bibliographic record
Abstract
This study was conducted to verify the performance of a recently developed subjective rating (SR) exposure assessment technique and to compare estimates made using this and two other techniques (trade mean, or TM, and task-based, or TB, approaches) to measured exposures. Subjects (n = 68) each completed three full-shift noise measurements over 4 months. Individual measured mean exposures were created by averaging each subject's repeated measurements, and TM, TB, and SR estimates were created using noise levels from worksites external to the current study. The bias, precision, accuracy, and absolute agreement of estimates created using the three techniques were evaluated by comparing estimated exposures with measured exposures. Trade mean estimates showed little bias, while neither the TM nor the SR techniques produced unbiased estimates, and the SR estimates showed the greatest bias of the three techniques. Accuracy was essentially equivalent among the three techniques. All three techniques showed poor agreement with measured exposures and were not highly correlated with each other. Estimates from the SR technique generally performed similarly to the TM and TB techniques. Methods to incorporate information from each technique into exposure estimates should be explored.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".