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Record W2037927878 · doi:10.1080/15298660108984621

Benzene and Total Hydrocarbons Exposures in the Downstream Petroleum Industries

2001· review· en· W2037927878 on OpenAlexafffund
Dave K. Verma, Diane M. Johnson, M. Lorraine Shaw, Karen des Tombe

Bibliographic record

VenueAIHAJ - American Industrial Hygiene Association · 2001
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcMaster University
FundersShell Canada
KeywordsOil refineryEnvironmental scienceTruckOccupational exposurePetroleumExposure assessmentPetroleum industryPetroleum productDownstream (manufacturing)Occupational hygieneRefineryWaste managementEnvironmental healthEnvironmental engineeringEngineeringOperations managementOccupational safety and healthMedicineChemistry

Abstract

fetched live from OpenAlex

A review of studies, including both articles published in peer-reviewed journals and reports that were not peer reviewed, regarding occupational exposure to benzene and total hydrocarbons in the downstream petroleum industry operations was performed.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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.129
GPT teacher head0.450
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2001
Admission routes2
Has abstractyes

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