P317-WE PAH DISTRIBUTIONS IN THE FRASER RIVER BASIN AND ARCTIC OCEAN: NEW INSIGHTS INTO THE USE OF PAH RATIOS AS INDICATORS OF PAH SOURCE AND COMPOSITION
Bibliographic record
Abstract
In recent sediments the parent and alkyl-substituted PAHs may derive from anthropogenic (fossil fuels and combustion) and natural sources (oil seeps, bitumens, coal, plant debris, forest and prairie fires). Since the PAH compositions of the two sources overlap, especially for parent PAHs, the significance of anthropogenic PAH in the environment must be evaluated against a dynamic background of natural PAH. Despite this source dichotomy, PAHs provide some of the most definitive and ubiquitous tracers of organic matter in aquatic systems (Yunker et al., 2002, and references therein). Sediment and suspended particulate samples used for this study have been obtained from the Fraser River basin, B.C., Canada (Yunker et al., 2002), the Beaufort, Chukchi, Barents and Laptev Seas, and from the central Arctic Ocean basins (Canadian, Eurasian and Greenland Sea basins). PAH analyses were performed by Axys Analytical Services Ltd. of Sidney, B.C. using selective ion monitoring GC/MS. The original laboratory chromatograms from Axys were used to obtain concentration data for all of the mass 276 and 278 PAHs and 1,7- and 2,6-dimethylphenanthrene. Literature for the last 6 years has been reviewed to update the characteristic PAH ratios for An/(Pn + An), Fl/(Fl + Py), BaA/(BaA + Ch) and IP/(IP + Bghi) for petroleum, single-source combustion and environmental samples presented in Yunker et al., (2002), and to add source PAH ratios for BF/(BF + BeP) (see PAH abbreviations below). PAHs in Arctic Ocean sediments demonstrate a continuum of inputs that range from primarily petroleum (the Beaufort Sea) to primarily combustion (the Barents Sea) and also exhibit negligible anthropogenic input. Such a data set is ideal for calibrating PAH ratios for PAHs with little or no literature source data available. In the Arctic Ocean data set there are significant (p < 0.001) linear relationships
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".