Forensic fingerprinting and source identification of the 2009 Sarnia (Ontario) oil spill
Bibliographic record
Abstract
This paper presents a case study in which integrated forensic oil fingerprinting and data interpretation techniques were used to characterize the chemical compositions and determine the source of the 2009 Sarnia (Ontario) oil spill incident. The diagnostic fingerprinting techniques include determination of hydrocarbon groups and semi-quantitative product-type screening via gas chromatography (GC), analysis of oil-characteristic biomarkers and the extended suite of parent and alkylated PAH (polycyclic aromatic hydrocarbon) homologous series via gas chromatography-mass spectrometry (GC-MS), determination and comparison of a variety of diagnostic ratios of "source-specific marker" compounds, and determination of the weathering degree of the spilled oil, and whether the spilled oil hydrocarbons have been mixed with any other "background" chemicals (biogenic and/or pyrogenic hydrocarbons). The detailed chemical fingerprinting data and results reveal the following: (1) all four samples are mixtures of diesel and lubricating oil with varying percentages of diesel to lube oil. Both samples 1460 and 1462 are majority diesel-range oil mixed with a smaller portion of lube oil. Sample 1461 contains slightly less diesel-range oil. Sample 1463 is majority lubricating-range oil. (2) The diesel in the four diesel/lube oil mixture samples was most likely the same diesel and from the same source. (3) The spill sample 1460 and the suspected-source sample 1462 have nearly identical concentrations and distribution patterns of target analytes including TPHs, n-alkane, PAHs and biomarker compounds; and have nearly identical diagnostic ratios of target compounds as well. Furthermore, a perfect "positive match" correlation line (with all normalized ratio data points falling into the straight correlation line) is clearly demonstrated. It is concluded that the spill oil water sample 1460 (#1, from the water around the vessel enclosed by a boom) matches with the suspected source sample 1462 (#3, from the vessel engine room bilge pump). (4) From the n-alkane and PAH analysis, it appears that the oil in the spill sample 1460 is slightly more weathered in comparison with sample 1462. The minor differences in fingerprints of two samples were most likely caused by weathering effects. (5) Sample 1461 (#2, from the vessel engine room bilge) and sample 1463 (#4, from the vessel bilge waste collection tank) demonstrated significantly different fingerprints and diagnostic ratios of target compounds from that of spill sample 1460. This was caused most likely by percentages of diesel to lube oil in these two samples different from that in spill sample 1460.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".