A generic system dynamics model for simulating and evaluating the hydrological performance of reconstructed watersheds
Bibliographic record
Abstract
Abstract. The mining of oil sands in northern Alberta, Canada, involves the stripping and salvage of surface soil layers to gain access to the oil mines. The oil sands industry has committed to reconstructing these disturbed watersheds to replicate the performance of the natural soil horizons and to reproduce the various functions of natural watersheds. The selection of the texture and thickness of the reconstructed soil cover layers is based primarily on the concept that all covers must have sufficient moisture for vegetation over the growing season. Assessment of the hydrological performance of the reconstructed soil covers is crucial to select the best cover alternative. A generic system dynamics watershed (GSDW) model is developed, based on the existing site-specific SDW model, and applied to five reconstructed watersheds located in the Athabasca mining basin, Alberta, Canada; and one natural watershed (boreal forest) located in Saskatchewan, Canada; to simulate the various hydrological processes; in particular, soil moisture patterns and actual evapotranspiration, in reconstructed and natural watersheds. The model is capable of capturing the dynamics of the water balance components in both reconstructed and natural watersheds. The developed GSDW model provides a vital tool, which enables the investigation of the utility of different soil cover alternative designs and evaluation of their performance. Moreover, the model can be used to conduct short- and long- term predictions under different climate scenarios.
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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 | 0.002 | 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".