Aromatic Steroids in Crude Oils and Petroleum Products and Their Applications in Forensic Oil Spill Identification
Bibliographic record
Abstract
Aromatic steroids including monoaromatic (MAS) and triaromatic steroids (TAS) are a series of naphthenoaromatic hydrocarbons, which consist of mixed structures of aromatic and saturated 6-carbon or 5-carbon rings. Although these aromatic steroids are in relatively low concentration in oils, their specific fingerprints and high weathering resistance make them desirable biomarkers for the characterization, correlation, differentiation, and source identification in environmental forensic investigations of oil spills. This study presents a quantitative GC/MS analysis of these aromatic hydrocarbons in a number of crude oils and refined petroleum products including light and mid-range distillate fuels, heavy fuels, and lubricating oils collected from various sources. TAS-cholestanes (C26), TAS-ergostanes (C27), and TAS-stigmastanes (C28) are the most distinguishable triaromatic steroids in most oil samples. C26 TAS-cholestane (20R) and C27 TAS-ergostane (20S) are coeluted and often present as the highest peak in m/z 231 chromatograms. A cluster of aromatic steroids were determined in nearly all the studied crude oils at various concentrations. These compounds are generally not detected in light fuel oil like gasoline and light diesel, but at variable abundance in heavy fuels and lubricating oils. In order to better understand the occurrence of aromatic steroids in refined petroleum products, a crude oil was compared with its products of laboratory fluid catalytic cracking (FCC) and hydrotreating processes. The effects of evaporative weathering and biodegradation on these compounds were evaluated with suites of weathered oil samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".