GuLF DREAM: A Model to Estimate Dermal Exposure Among Oil Spill Response and Clean-up Workers
Bibliographic record
Abstract
Tens of thousands of individuals performed oil spill response and clean-up (OSRC) activities following the 'Deepwater Horizon' oil drilling rig explosion in 2010. Many were exposed to oil residues and dispersants. The US National Institute of Environmental Health Sciences assembled a cohort of nearly 33 000 workers to investigate potential adverse health effects of oil spill exposures. Estimates of dermal and inhalation exposure are required for those individuals. Ambient breathing-zone measurements taken at the time of the spill were used to estimate inhalation exposures for participants in the GuLF STUDY (Gulf Long-term Follow-up Study), but no dermal measurements were collected. Consequently, a modelling approach was used to estimate dermal exposures. We sought to modify DREAM (DeRmal Exposure Assessment Method) to optimize the model for assessing exposure to various oil spill-related substances and to incorporate advances in dermal exposure research. Each DREAM parameter was reviewed in the context of literature published since 2000 and modified where appropriate. To reflect the environment in which the OSRC work took place, the model treatment of evaporation was expanded to include vapour pressure and wind speed, and the effect of seawater on exposure was added. The modified model is called GuLF DREAM and exposure is estimated in GuLF DREAM units (GDU). An external validation to assess the performance of the model for oils, tars, and fuels was conducted using available published dermal wipe measurements of heavy fuel oil (HFO) and dermal hand wash measurements of asphalt. Overall, measured exposures had moderate correlations with GDU estimates (r = 0.59) with specific correlations of -0.48 for HFO and 0.68 for asphalt. The GuLF DREAM model described in this article has been used to generate dermal exposure estimates for the GuLF STUDY. Many of the updates made were generic, so the updated model may be useful for other dermal exposure scenarios.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".