Atmospheric sulfur and nitrogen deposition in the Athabasca oil sands region is correlated with foliar nutrient levels and soil chemical properties
Bibliographic record
Abstract
The oil extraction industry and human activity in north eastern Alberta has been growing steadily since the 1960’s and is a source of air pollution. In the late 1990’s the Wood Buffalo Environmental Association was established to monitor air quality for both public and environmental health. A primary environmental concern was soil acidification caused by sulfur (S) and nitrogen (N) deposition. A network of forest health monitoring (FHM) sites was established in dry jack pine ecosystems to serve as an early indicator of negative impacts. A sampling campaign was executed in 2011 and this study examines soil properties and foliar nutrients in the context of measured and modeled acid deposition. Total N (TN), SO42−, pH, base cation to aluminum ratio (BC:Al), and base saturation (% BS) are reported for the organic layer (LFH) and 3 depths in the mineral soil, while foliar nutrients were analysed from current annual growth in jack pine needles. Atmospheric deposition of S, N, BC, and potential acid input (PAI) in the study area was recently provided by Edgerton et al. (2020) and soil and foliar chemistry was evaluated based on deposition estimates and measurements. Inverse distance weighting was used to examine spatial patterns and regression analysis was used to quantify relationships between variables. The results indicated that S deposition is spatially correlated with foliar SO42− concentration, and LFH SO42−, but not mineral topsoil (0–5 cm) SO42−. Nitrogen deposition was spatially correlated with foliar N concentration, but not LFH or topsoil TN indicating potential uptake by the foliage or rapid uptake by roots in the LFH layer. High BC deposition in the same areas with the highest potential acid inputs (PAI) did not correlate significantly with changes in soil pH. However, LFH pH was significantly related to dry NH3 deposition, which has not been reported previously and requires further investigation.
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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 | 0.000 | 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) | 0.001 | 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".