the MAGIC model to soils and lakes in the Athabasca Oil Sands Region, Alberta
Bibliographic record
Abstract
The effects-based acid emissions management framework (EMF) for determining the need for emission control policies in the Athabasca Oil Sands Region, Canada is dependent on model simulations of future soil and surface water chemistry. An approach for regional application of the Model of Acidification of Groundwater in Catchments (MAGIC) was developed that addresses the differential sensitivity of forest soils and lakes. The approach used was a dual application wherein a plot-scale calibration to forest soils and a catchment-based calibration to lake chemistry were used to account for poorly understood hydrologic connections between uplands and lakes, key processes including sulphur (S) and nitrogen (N) retention as well as groundwater sources of base cations to the lakes. The regional application was carried out at 50 lake catchments currently monitored for response to acid deposition. Simulated forest soil chemistry (modelled at 28 catchments) exhibited small changes in base saturation under future conditions of elevated acid deposition, while in general molar BC:Al exhibited considerable change but remained well above critical chemical limits used to protect acid-sensitive forest soils. Similarly, simulations of charge balance acid neutralizing capacity (ANCCB) for the lakes suggested very small decreases since industrialization, and forecast projections under acid deposition double the current level suggested that only one lake will reach the critical threshold for ANCCB (75 µeq L–1) specified by the EMF. There is
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".