Modeling Soil Acidification in the Athabasca Oil Sands Region, Alberta, Canada
Bibliographic record
Abstract
Industrial activities have proliferated across Canada's Athabasca Oil Sands Region in recent years, stimulating concerns over the impact of atmospheric sulfur dioxide (SO2) emissions on acid-sensitive terrain. Upland jack pine forests have been identified as possibly the most sensitive ecosystem in the region but despite high emissions of SO2, sulfur (S) deposition is relatively low across much of the region. The response of forest soils at 11 locations that exhibit low estimated weathering rates (< 10 mmol(c) x m(-2) x yr(-1)) was simulated for the period 1900-2100 using a dynamic hydrogeochemical model assuming no change or doubling of S deposition. The model predicted minimal impact on soil base saturation (BS), but a decline in soil solution base cation (BC) to aluminum (Al) ratio (BC:Al). The regional effects-based emissions management framework uses modeled changes in these two parameters relative to site-specific chemical thresholds to trigger actions to reduce S emissions. Modeled changes in BS are insufficient to invoke a response. Under base case conditions, modeled BC:Al reaches the chemical threshold at two and three sites within 15 and 30 years, respectively. Under conditions of double S deposition, seven sites are simulated to reach the threshold within 30 years. Nonetheless, the chemical thresholds are stringent relative to critical chemical criteria used elsewhere and the impacts of acidic deposition in the region are anticipated to be limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".