Inter-annual variations in water yield to lakes in northeastern Alberta: implications for estimating critical loads of acidity
Bibliographic record
Abstract
Stable isotopes of water were applied to estimate water yield to fifty lakes in northeastern Alberta as part of an acid sensitivity study underway since 2002 in the Athabasca Oil Sands Region (AOSR). Herein, we apply site-specific water yields for each lake to calculate critical loads of acidity using water chemistry data and a steady-state water chemistry model. The main goal of this research was to improve site-specific critical load estimates and to understand the sensitivity to hydrologic variability across a Boreal Plains region under significant oil sands development pressure. Overall, catchment water yields were found to vary significantly over the seven year monitoring period, with distinct variations among lakes and between different regions, overprinted by inter-annual climate-driven shifts. Analysis of critical load estimates based on site-specific water yields suggests that caution must be applied to establish hydrologic conditions and define extremes at specific sites in order to protect more sensitive ecosystems. In general, lakes with low (high) water yield tended to be more (less) acid sensitive but were typically less (more) affected by interannual hydrological variations. While it has been customary to use long-term water yields to define a static critical load for lakes, we find that spatial and temporal variability in water yield may limit effectiveness of this type of assessment in areas of the Boreal Plain characterized by heterogeneous runoff and without a long-term lake-gauging network. Implications for predicting acidification risk are discussed for the AOSR.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".