Forensic Fingerprinting of Biomarkers for Oil Spill Characterization and Source Identification
Bibliographic record
Abstract
Biomarkers are one of the most important hydrocarbon groups in petroleum. Biomarkers can be detected in low quantities (ppm and sub-ppm level) in the presence of a wide variety of other types of petroleum hydrocarbons by the use of the gas chromatography/mass spectrometry (GC/MS). Relative to other hydrocarbon groups in oil such as alkanes and most aromatic compounds, biomarkers are more degradation-resistant in the environment. Furthermore, biomarkers formed under different geological conditions and ages may exhibit different biomarker fingerprints. Therefore, chemical analysis of biomarkers generates information of great importance to environmental forensic investigations in terms of determining the source of spilled oil, differentiating and correlating oils, and monitoring the degradation process and weathering state of oils under a wide variety of conditions. This article briefly reviews biomarker chemistry, biomarker characterization and quantification, biomarker distributions, weathering effects on biomarker composition, bicyclic biomarker sesquiterpanes and diamondoids, diagnostic ratios and cross-plots of biomarkers, unique biomarkers, application of biomarker fingerprinting techniques for spill source identification, and application of multivariate statistical analysis for biomarker fingerprinting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".