A CASE STUDY: DISTINGUISHING PYROGENIC HYDROCARBONS FROM PETROGENIC HYDROCARBONS
Bibliographic record
Abstract
ABSTRACT A new diagnostic parameter of “Pyrogenic Index (PI),” defined as(other 3–6 ring EPA priority PAHs)/N(5 alkylated PAHs), has been proposed as a quantitative indicator for identification of pyrogenic PAHs and for differentiating pyrogenic from petrogenic PAHs. It has been well understood that petrogenic and pyrogenic PAHs are characterized by dominance of five alkylated PAH homologues and by dominance of unsubstituted high molecular weight PAHs, respectively. In comparison with traditional diagnostic ratios such as phenanthrene/anthracene (Ph/An), benz[a]anthracene/chrysene (BaA/Ch), and fluoranthene/pyrene (Fl/Py), the PI Index more truly reflects the difference in the PAH distribution between these two sets of PAHs. The PI Index has been successfully used as an effective criterion to unambiguously differentiate pyrogenic and petrogenic PAHs. In this paper a case study is presented to illustrate the utility of the PI index to distinguish the pyrogenic PAHs generated by burning from the petrogenic PAHs. On October of 2004, a fire accident happened in the HMCE Chicoutimi submarine at sea off the west coast of Ireland as the submarine was making its way to Halifax, Canada. In order to determine effects of the fire accident on the health of crew members, a number of fire samples were collected and sent to the ESTD for characterization. Sample characterization results clearly revealed that the distribution profiles of PAHs in the samples are combined signatures from both pyrogenic and petrogenic PAHs. The pyrogenic PAHs were generated from the fire accident, while the petrogenic PAHs came from contamination of petroleum products used by the submarine. The presence of petroleum hydrocarbons is further confirmed by the discovery of oil-characteristic n-alkanes and biomarker compounds in the fire samples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".