Reclamation for aspen revegetation in the Athabasca oil sands: Understanding soil water dynamics through unsaturated flow modelling
Bibliographic record
Abstract
Carrera-Hernández, J. J., Mendoza, C. A., Devito, K. J., Petrone, R. M. and Smerdon, B. D. 2012. Reclamation for aspen revegetation in the Athabasca oil sands: Understanding soil water dynamics through unsaturated flow modelling. Can. J. Soil Sci. 92: 103–116. Reclamation of mined areas in the Athabasca oil sands region is required by law, with the ultimate goal of revegetating to species characteristic of predisturbance native plant communities. To develop adequate reclamation strategies, an analysis of soil water dynamics is of utmost importance, as is understanding the impact of the thickness of the reclamation cover. In this work, soil water dynamics and fluxes at the water table were simulated for three reclamation scenarios and compared with the fluxes obtained for natural conditions assuming that aspen is the target reclamation species. According to the simulations, a reclamation thickness between 0.5 and 1.0 m can be used to provide water for revegetation. The numerical simulations show that the reclaimed landscapes have fluxes at the water table that exhibit less fluctuation than natural conditions. To limit the interaction between the water table and atmospheric fluxes, and to limit upward flux, the water table should be deeper than 2.0 m on reclaimed landscapes that use aspen for revegetation, particularly when reclamation takes place during a dry climatological cycle.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".