Forensic Fingerprinting of Diamondoids for Correlation and Differentiation of Spilled Oil and Petroleum Products
Bibliographic record
Abstract
Diamondoids (adamantanes and diamantanes) are rigid, three-dimensionallyfused cyclohexyl-ring alkane compounds that can be found in almost all crude oils and in most petroleum products. Forforensic environmental investigations, the most commonly used biomarkers are high molecular weight (MW) tri- to pentacyclic terpanes and steranes. Most of these high MW biomarkers, however, are removed from the original crude oil feedstocks during the refining processes, while smaller biomarkers including diamondoids are concentrated in petroleum products. Fingerprinting diamondoids could thus provide another diagnostic means for correlation and differentiation of spilled oils and be particularly valuable for light to midrange distillates, such as jet and diesel fuels, the source of which may be difficult to identify using routine biomarker techniques. In this work, a reliable GC-MS analytical method has been developed for characterization and quantitation of diamondoids. The method detection limits for five target diamondoids were determined to be in the range of 0.06-0.14 microg/g oil. Distributions of diamondoids in over 100 different oils and refined products were quantitatively compared. The concentrations of four groups of target biomarkers were found, in general, to decrease in the order of sesquiterpanes > terpanes and steranes > adamantanes > diamantanes in both crude oils and refined products. A number of indices of admantanes and diamantanes have been developed and assessed as source indicators using their diagnostic powers (DP). The effects of evaporative weathering and biodegradation on alteration of diamondoid distributions have been quantitatively investigated. Finally, a spill case study by statistical evaluation of diagnostic ratios using the "two-tailed" Student's tapproach is presented to illustrate the unique utility of diamondoids for correlation and differentiation of unknown spilled diesels.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".