Chromatographic Fingerprinting Analysis of Crude Oils and Petroleum Products
Bibliographic record
Abstract
This chapter overviews and discusses oil chemistry, analytical methodologies for separation and analysis of selected petroleum hydrocarbons in crude oils and various refined petroleum products, and identification and correlation of spilled oil to its source. The characterization, identification, and correlation are comprehensive and challenging tasks due to the wide variability in petroleum products. Forensic oil fingerprinting analysis becomes even more complicated once oil is released into the environment and subject to various weathering processes. It is extremely important to collect and sieve reliable evidence for each specific case. The most important criteria from quantitative chromatographic analysis are the concentrations, distribution profiles, and diagnostic ratios of source-specific petroleum compounds. The selection of diagnostic ratios should be based upon a particular spill case including the oil type, weathering condition, and the abundance and distribution of target compounds. Oil fingerprinting analysis has being advanced greatly in recent decades, thanks to the rapid development of analytical and statistical techniques. Although automatic analysis using computerized techniques and oil database could be a trend in the future, successful and defensible source identification still heavily relies on systematic analysis, meticulous examination, and scientific interpretation of all available information by experienced environmental forensic scientists.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".