Net percolation as a function of topographic variation in a reclamation cover over a saline-sodic overburden dump
Bibliographic record
Abstract
Surface mining of oil sands in northern Alberta requires stripping of saline-sodic shale overburden, which is typically placed in large upland overburden dumps.Due to the chemical nature of this shale, engineered soil covers must be constructed over the shale to support the growth of forest vegetation.A research site on South Bison Hill (SBH), a shale overburden dump at the Syncrude Canada Ltd. Mildred Lake Mine, has been used by researchers over the past decade to study the performance of a reclamation cover.This study was undertaken to improve the understanding of salt and moisture dynamics in the cover-shale system.In particular, the objective of this study was to develop an estimate of the net percolation rate through the cover soil and into the shale overburden.Stable isotope ( 2 H and 18 O) measurements obtained from the pore water of soil samples were used to develop stable isotope profiles at various sampling locations along the slope and plateau of the SBH.Simulated profiles were then generated using 2D, finite element numerical modelling software and compared to the measured profiles.Model parameters were obtained from testing and the work of previous researchers.The model results revealed that the net percolation is greatest (32-50 mm/yr) for the plateau and mid-slope bench sample locations.Net percolation rates for sample locations on the slope were lower at 0-12 mm/yr.The results from the stable isotope modelling were utilized in a SO 4 2-transport model to ascertain if calculated net percolation rates could explain measured salinity profiles.This modelling exercise revealed that calculated SO 4 2-profiles are highly dependent on the assumed SO 4 2-production rates in the shale, which is primarily attributed to pyrite oxidation.The model results showed the isotope-based net percolation rates could explain the measured SO 4 2profiles for a reasonable range SO 4 2-production rates.The SO 4 2-production rates calculated in the model were greatest for the plateau and midslope bench locations and lesser for the sloped locations.The model also showed that the mass of SO 4 2-removed by interflow was minimal compared to the mass generated by pyrite oxidation and that net percolation is the dominant flushing mechanism at net percolation rates of 8 mm/yr or more.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".