Evaluating officially reported polycyclic aromatic hydrocarbon emissions in the Athabasca oil sands region with a multimedia fate model
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
Emissions of organic substances with potential toxicity to humans and the environment are a major concern surrounding the rapid industrial development in the Athabasca oil sands region (AOSR). Although concentrations of polycyclic aromatic hydrocarbons (PAHs) in some environmental samples have been reported, a comprehensive picture of organic contaminant sources, pathways, and sinks within the AOSR has yet to be elucidated. We sought to use a dynamic multimedia environmental fate model to reconcile the emissions and residue levels reported for three representative PAHs in the AOSR. Data describing emissions to air compiled from two official sources result in simulated concentrations in air, soil, water, and foliage that tend to fall close to or below the minimum measured concentrations of phenanthrene, pyrene, and benzo(a)pyrene in the environment. Accounting for evaporative emissions (e.g., from tailings pond disposal) provides a more realistic representation of PAH distribution in the AOSR. Such indirect emissions to air were found to be a greater contributor of PAHs to the AOSR atmosphere relative to reported direct emissions to air. The indirect pathway transporting uncontrolled releases of PAHs to aquatic systems via the atmosphere may be as significant a contributor of PAHs to aquatic systems as other supply pathways. Emission density estimates for the three PAHs that account for tailings pond disposal are much closer to estimated global averages than estimates based on the available emissions datasets, which fall close to the global minima. Our results highlight the need for improved accounting of PAH emissions from oil sands operations, especially in light of continued expansion of these operations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it