Source Apportionment of Background PAHs in the Peace-Athabasca Delta (Alberta, Canada) Using Molecular Level Radiocarbon Analysis
Bibliographic record
Abstract
The downstream accumulation of polycyclic aromatic hydrocarbons (PAHs) in the Peace-Athabasca Delta (PAD), an ecologically important landscape, is a key issue of concern given the rapid development of the oil sands industry in Northern Alberta, Canada. In addition to PAHs derived from industrial activity (i.e., oil sands mining) within the Athabasca watershed, however, forest fires and erosion of fossil fuel deposits within both the Athabasca and Peace watersheds are two potentially important natural sources of PAHs delivered to the PAD. Consequently, evaluating the environmental impact of mining activities requires a quantitative understanding of natural, background PAHs. Here, we utilize molecular-level natural-abundance radiocarbon measurements on an amalgamated sediment record from a Peace River flood-susceptible oxbow lake in the northern Peace sector of the PAD to quantitatively discriminate sources of naturally occurring alkylated PAHs (fossil and modern biomass). A radiocarbon mass balance quantified a predominantly natural petrogenic source (93% petrogenic, 7% forest fire) for alkylated PAHs during the past ∼50 years. Additionally, a significant petrogenic component determined for retene, a compound usually considered a biomarker for softwood combustion, suggests that its use as a unique forest fire indicator may not be suitable in PAD sediments receiving Peace watershed-derived fluvial inputs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".