Integrated analysis of petroleum biomarkers and polycyclic aromatic compounds in lake sediment cores from an oil sands region
Bibliographic record
Abstract
We examined polycyclic aromatic compounds (PACs) and petroleum biomarkers (steranes, hopanes, and terpanes) in radiometrically-dated lake sediment cores from the Athabasca oil sands region (AOSR) and the Peace-Athabasca Delta (PAD) region in Alberta (Canada) to determine whether contributions from petroleum hydrocarbons have changed over time. Two floodplain lakes in the PAD (PAD 30, PAD 31) recorded increased flux of alkylated PACs and increased petrogenic (petroleum-derived) hydrocarbons after ∼1980, coincident with a decline of sediment organic carbon content and a rise of bulk sedimentation rate, likely due to increased Athabasca River flow. A large expansion of upstream oilsands mining, upgrading, and refining may also have contributed to the observed shift to more petrogenic hydrocarbons to sediments since the 1980s. Alkylated PAC flux increased in the floodplain lake analyzed within the AOSR (Saline Lake) since the 1970s-1980s, coincident with a sharp rise in sediment organic carbon content and increased contributions of petrogenic hydrocarbons. These changes identify increased supply of petrogenic PACs occurred as Athabasca River floodwaters waned, and may implicate aerial contributions of petrogenic hydrocarbons from oilsands activity. PACs and petroleum biomarkers (steranes, hopanes, and terpanes) in sediment cores from Saline Lake, PAD 30 and PAD 31 revealed a predominance of petrogenic hydrocarbons in these lakes. In contrast, we recorded minimal petrogenic hydrocarbons in the reference lakes outside the surface minable area of the AOSR and PAD (Mariana Lake and BM11), though we noted slight increases in petrogenic contributions to modern (2010-2016) sediments. We show how a combined analysis of PACs and petroleum biomarkers in sediments is useful to quantify petrogenic contributions to lakes with added confidence and highlight the potential for petroleum biomarkers in lake sediment cores as a novel and effective method to track petroleum hydrocarbons in lake sediment.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".