Application of Light Petroleum Biomarkers for Forensic Characterization and Source Identification of Spilled Light Refined Oils
Bibliographic record
Abstract
Light petroleum biomarkers such as bicyclic sesquiterpanes and diamondoids are ubiquitous components of crude oils and ancient sediments, and are also widely found in intermediate petroleum distillates and many finished petroleum products. These compounds are relatively resistant to biodegradation and light-to-medium evaporation weathering, thus particularly useful in oil-source correlation and differentiation for those cases where the traditional tri- to pentacyclic biomarkers are absent. This work utilized sesquiterpanes and diamondoids for fingerprinting and identification of light oils spilled on water. The gas chromatography/flame ionization detection (GC/FID) analysis and distribution profiles of polycyclic aromatic hydrocarbon (PAHs) and conventional biomarkers suggest that the spilled oils are mixtures of mainly gasoline and light diesel type fuel. Since potential source oil candidates were not available, and a large part of the hydrocarbons in gasoline and diesel co-eluted in chromatographic analysis, it is a challenge to quantify the gasoline and diesel in spill samples. It has been known from previous studies that the bulk concentrations of C14 to C16 sesquiterpanes are in the range of approximately 6,000 to 9,000 μg/g for many light diesel fuels, while little or no sesquiterpanes were detected in gasoline, light kerosene and heavy-end lubricating oils. The target sesquiterpanes in the spilled oil samples were determined to be in quite high concentrations: approximately 4,000 μg/g oil. Therefore, it was estimated that these spilled oil samples consist of approximately half gasoline and half light diesel. To verify the estimation, spilled samples were simulated by mixing a fresh gasoline and a light diesel with a similar carbon range as the spilled oils. Results from comparison of GC/FID chromatograms of the spilled oils with the simulated spill samples are consistent with that obtained from sesquiterpane analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".