Integrated Simulation of Polycyclic Aromatic Compounds in the Athabasca River Basin
Bibliographic record
Abstract
The environmental risks posed by polycyclic aromatic compounds (PACs) associated with increasing oil and mineral mining activities have become a major concern in Alberta. Due to their diversity and complex behavior, basin-scale surface water PACs simulation based on traditional modeling tools is restricted, thereby hindering decision-making. To address this, we designed an integrated simulation framework that combines predictive relationships among PACs with mechanism-based models and implemented it in the Athabasca River basin (ARB) in Alberta. The predictive relationships were obtained through a preliminary analysis based on principal component analysis, clustering, and regression. For mechanism-based simulation, a Python-based Soil Water Assessment Tool-Load Calculator (SWAT-LC) was developed and coupled with SWAT and the Water Quality Analysis Simulation Program 8 (WASP8) to describe the fate and transport of PACs in both the terrestrial and aquatic systems. Our results show that: 1) Out of 76 PACs studied, two clusters were identified, including one with 66 PACs exhibiting seasonal patterns, and another with 10 PACs marked by significant uncertainties. Chrysene and naphthalene were chosen from the respective cluster as representatives for mechanism-based modeling; 2) The established mechanism-based model demonstrated overall acceptable to satisfactory performance for chrysene at different sites (NSE=-0.28~0.33, d=0.34~0.71, PBIAS=0.09%~36.68%), although was less successful in describing the fluctuations of naphthalene; 3) Evidence indicates that seasonalities in PACs are petrogenic and are mainly driven by soil-water processes, while surface wash-off in the oil sands region and wet depositions lead to concentration spikes in river water; 4) The predictive relationships of the other 74 PACs are robust along the Athabasca River mainstem, showing great potential for facilitating rapid decision-making in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".