Modelling catchment response to acid deposition: a regional dual application of the MAGIC model to soils and lakes in the Athabasca Oil Sands Region, Alberta
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 limited potential for acidification impacts at the study sites.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it