Forensic identification of spilled biodiesel and its blends with petroleum oil based on fingerprinting information
Bibliographic record
Abstract
A case study is presented for the forensic identification of several spilled biodiesels and its blends with petroleum oil using integrated forensic oil fingerprinting techniques. The integrated fingerprinting techniques combined SPE with GC/MS for obtaining individual petroleum hydrocarbons (aliphatic hydrocarbons, polyaromatic hydrocarbons and their alkylated derivatives and biomarkers), and biodiesel hydrocarbons (fatty acid methyl esters, free fatty acids, glycerol, monoacylglycerides, and free sterols). HPLC equipped with evaporative scattering laser detector was also used for identifying the compounds that conventional GC/MS could not finish. The three environmental samples (E1, E2, and E3) and one suspected source sample (S2) were dominant with vegetable oil with high acid values and low concentration of fatty acid methyl ester. The suspected source sample S2 was responsible for the three spilled samples although E1 was slightly contaminated by petroleum oil with light hydrocarbons. The suspected source sample S1 exhibited with the high content of glycerol, low content of glycerides, and high polarity, indicating its difference from the other samples. These samples may be the separated byproducts in producing biodiesel. Canola oil source is the most possible feedstock for the three environmental samples and the suspected source sample S2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".