Modelling boreal lake catchment response to anthropogenic acid deposition
Bibliographic record
Abstract
A dynamic hydrogeochemical model of water acidification (MAGIC: Model of Acidification of Groundwater in Catchments) was applied to two catchments with contrasting hydrological influences in the Athabasca Oil Sands Region of Alberta to predict catchment response to elevated levels of acidic deposition. Key processes that determine catchment response to atmospheric deposition, including groundwater base cation inputs and retention of sulphur (S) in peatland complexes were parameterized in the model. Although deposition of S and nitrogen (N) in the region has increased over the last 40 years, levels are low at the study sites relative to impacted areas of eastern North America. Model forecasts for the period 2005–2100 were run under constant 2005 deposition levels (base case) and at acid deposition double this level. Simulated past and future soil base saturation was constant over the course of the 200 year (1900–2100) modelled period. At the lake with high charge balance acid neutralizing capacity (ANCCB), where large base cation sources dominate lake chemistry, little change in surface water chemistry was predicted under either forecast scenario. Under the double acid forecast scenario at the low ANCCB lake, simulated lake ANCCB decreased in response to elevated S deposition, but the magnitude of decrease was comparable to the range in observational data. The simulations suggest limited risk of acidification, primarily due to S retention in the catchments, but the potential for drought-induced episodic depression of ANCCB may be important on this landscape.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".