Investigating the fate of polycyclic aromatic sulfur heterocycle compounds in spilled oils with a microcosm weathering experiment
Bibliographic record
Abstract
Abstract One of the common toxic compound groups in crude oils are the polycyclic aromatic sulfur heterocycles (PASHs) and their related alkylated forms (APASHs). Unlike commonly investigated polycyclic aromatic hydrocarbons (PAHs) and their alkylated forms (APAHs), these sulfur containing compounds have not been extensively studied due to the lack of practical analytical methodology as well as expense and limited availability of chemical standards. In the current study, a newly developed polycyclic aromatic carbon (PAC) method was applied to analyze the various PASHs/APASHs in crude oil samples using PAHs as surrogate standards. To investigate the fate of PASHs/APASHs in spilled oils in the environment, microcosm systems containing various crude oils were prepared and exposed to the environment for two months, simulating the summer weather conditions of Canada’s west coast. The artificially weathered crude oil samples were analyzed for both PAH/APAH and PASH/APASH composition, and the results were compared to un-weathered counterparts of the oils. PASHs/APASHs were found to be affected by the microcosm weathering in similar ways to PAHs/APAHs. Fifteen PASHs and APASHs were found to be resistant to weathering and be potential candidates as biomarkers in oil spill forensic investigation.
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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) | 0.000 | 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 | 0.000 |
| 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".