A CASE STUDY: DISTINGUISHING PYROGENIC HYDROCARBONS FROM PETROGENIC HYDROCARBONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it