Characterization, Weathering, and Application of Sesquiterpanes to Source Identification of Spilled Lighter Petroleum Products
Bibliographic record
Abstract
Biomarkers have become increasingly important for identifying the source of spilled oil, due to their specificity and high resistance to biodegradation. The biomarkers most commonly used in forensic investigations are the high molecular weight (MW) tri- and pentacyclic terpanes and steranes. For lighter petroleum products such as jet fuels and diesels, the refining processes remove most high MW biomarkers from the original crude oil feedstock. The smaller bicyclic sesquiterpanes, however, are concentrated in these products. Sesquiterpanes are ubiquitous components of crude oils and ancient sediments. Examination of GC-MS chromatograms of these bicyclic biomarkers using their characteristic fragment ions (m/z 123, 179, 193, and 207) provides a highly diagnostic means for identifying spilled oil, particularly for lighter refined product samples that are difficult to identify by current techniques. In this work, sesquiterpanes in crude oils and petroleum products are identified and characterized, distributions of sesquiterpanes in oils and refined products are compared, the effects of evaporative weathering on sesquiterpane distributions are examined, and a methodology using diagnostic indices of sesquiterpanes is developed for oil correlation and differentiation. Finally, two case studies are presented to illustrate the unique utility of sesquiterpanes for fingerprinting and identifying unknown diesel spills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".