System dynamics approach to assess the sustainability of reclamation of disturbed watersheds
Bibliographic record
Abstract
The mining of oil sands in northern Alberta leaves behind large open pits, tailings, and overburden piles in which the surface and subsurface hydrology has been completely disrupted. Extensive reclamation work is required to reconstruct the entire landscape and reestablish the various elements of the hydrologic cycle. Syncrude Canada Ltd. has established a series of small instrumented watersheds in a reclaimed overburden pile at the Mildred Lake mine in northern Alberta, Canada, to test the sustainability of different reclamation strategies. The purpose of these field sites is to assess the performance of different reclamation strategies and track the evolution of the reclaimed landscape with time. The saline–sodic shale overburden has been covered with different (in type and depth) soil layers to provide sufficient moisture storage for vegetation while minimizing runoff and salt transport into the cover from the underlying overburden shale. In this paper, a system dynamics watershed model (SDWM) is developed to simulate one of the reconstructed watersheds and assess its ability to provide common watershed functions. The model is at an early stage, but preliminary results point to the potential of the system dynamics approach in simulating watersheds and testing different scenarios. The tested reclamation strategy seems to be satisfactory within a certain range of hydrologic conditions. Further validation of the SDWM is required, however, before relying on its results for decision support with regard to reclamation strategies.Key words: sustainability, watershed simulation, system dynamics, reclamation, STELLA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".