SOURCE IDENTIFICATION OF SPILLED DIESEL USING DIAGNOSTIC SESQUITERPANES AND DIAMONDOIDS
Bibliographic record
Abstract
ABSTRACT Examination of GC-MS chromatograms of bicyclic biomarker sesquiterpanes and diamondoids using their characteristic fragment ions provides another highly diagnostic means for correlation and differentiation of unknown spilled oil samples that are difficult to identify by current fingerprinting techniques. In this work, GC-FID and GC-MS fingerprinting analysis in conjunction with statistical data analysis of target oil hydrocarbons including sesquiterpanes and diamondoids was performed for identification of an unknown oil spill incident occurred in a harbor of the Netherlands in 2004. Forensic investigation included the: (1) identification and characterization of numerous sesquiterpanes and diamondoids; (2) comparison of the distribution of sesquiterpanes and diamondoids; (3) development of a series of diagnostic indices for correlation and differentiation of spilled fuel samples; and (4) use of “Two-tailed” unpaired Student'S t-test to statistically evaluate the imperceptible differences between the selected diagnostic ratios of target compounds in the spill fuel and the suspected source fuel samples. At a 95% confidence interval, 33 of all 38 diagnostic indices (among them, 20 are diagnostic indices of sesquiterpanes and diamondoids) show positive matches between spill sample and suspected source fuel sample 1. In comparison, only 5 of 38 diagnostic indices indicate positive matches between spill sample and suspected source sample 3. These evidences demonstrate that the bunker ship has the responsibility for this oil spill incident.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".