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Record W2022498970 · doi:10.1080/15298660008984534

Benzene and Total Hydrocarbon Exposures in the Upstream Petroleum Oil and Gas Industry

2000· article· en· W2022498970 on OpenAlexaffabout
Dave K. Verma, Diane M. Johnson, James D. McLean

Bibliographic record

VenueAIHAJ - American Industrial Hygiene Association · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcMaster UniversityShell (Canada)Hamilton Health Sciences
Fundersnot available
KeywordsPetroleum industryBenzeneGasolinePetroleumEnvironmental scienceWaste managementPetroleum productUpstream (networking)KeroseneOccupational exposure limitOccupational exposureFuel oilEnvironmental engineeringChemistryEngineeringMedicineEnvironmental healthOrganic chemistry

Abstract

fetched live from OpenAlex

Occupational exposures to benzene and total hydrocarbons (THC) in the Canadian upstream petroleum industry are described in this article. A total of 1547 air samples taken by 5 oil companies in various sectors (i.e., conventional oil/gas, conventional gas, heavy oil processing, drilling and pipelines) were evaluated and summarized. The data includes personal long- and short-term samples and area long-term samples. The percentage of samples over the occupational exposure limit (OEL) of 3.2 mg/m3 or one part per million for benzene, for personal long-term samples ranges from 0 to 0.7% in the different sectors, and area long-term samples range from 0 to 13%. For short-term personal samples, the exceedance for benzene is at 5% with respect to the OEL of 16 mg/m3 or five parts per million in the conventional gas sector and none in the remaining sectors. THC levels were not available for all sectors and had limited data points in others. The percentage exceedance of the OEL of 280 mg/m3 or 100 parts per million for THC as gasoline ranged from 0 to 2.6% for personal long-term samples. It is recommended that certain operations such as glycol dehydrators be carefully monitored and that a task-based monitoring program be included along with the traditional long- and short-term personal exposure sampling.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations34
Published2000
Admission routes2
Has abstractyes

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