Benzene and Total Hydrocarbons Exposures in the Downstream Petroleum Industries
Bibliographic record
Abstract
A review of studies, including both articles published in peer-reviewed journals and reports that were not peer reviewed, regarding occupational exposure to benzene and total hydrocarbons in the downstream petroleum industry operations was performed. The objective was to provide a broad estimate of exposures by compiling exposure data according to the following categories: refinery, pipeline, marine, rail, bulk terminals and trucks, service stations, underground storage tanks, tank cleaning, and site remediations. The data in each category was divided into personal occupational long-term and short-term samples. The summarized data offers valuable assistance to hygienists by providing them with an estimate and range of exposures. The traditional 8-hour time-weighted average (TWA) exposure and the 40-hour workweek do not generally coincide with exposure periods applicable to workers in marine, pipeline, railcar, and trucking operations. They are more comparable with short-term exposure or task-based exposure assessments. The marine sector has a large number of high exposures. Although relatively few workers are exposed, their exposures to benzene and total hydrocarbons are sometimes an order of magnitude higher than the respective exposure limits. It is recommended that in the future, it would be preferable to do more task-based exposure assessments and fewer traditional TWA long-term exposure assessments within the various sectors of the downstream petroleum industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".