Characterizing the hydraulic and transport properties of a constructed coarse tailings sand aquifer
Bibliographic record
Abstract
Millions of tonnes of coarse tailings sand are produced every year as a byproduct of the bitumen extraction process in the Athabasca Oil Sands Region. These tailings materials contain residual quantities of mobile solutes, which can be transported through groundwater to downgradient terrestrial and aquatic ecosystems. The anticipated ubiquity of coarse tailings sand on the post-mined landscape necessitates the characterization of its hydraulic and transport properties. Hydraulic conductivity and dispersivity was evaluated at multiple scales, and included the first field-scale tracer test conducted in a tailings sand aquifer. Average hydraulic conductivity derived using laboratory cores, single-well response tests, and the tracer test were 3.2 m d−1, 2.9 m d−1, and 3.4 m d−1, respectively. These measurements demonstrated close agreement and were consistent with expectations of a material that experiences some grain-size segregation and homogenization due to the oil sands process and the nature of deposition. The field-scale tracer test appeared to obtain the asymptotic dispersivity of the coarse tailings sand aquifer, reaching a maximum value of 0.5 m after 18 m of displacement. Coarse tailings in the oil sands that experience similar processes of segregation, settling, and deposition on the reclamation landscape could be expected to have similar hydraulic properties.
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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 | 0.000 | 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 | 0.001 |
| 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 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".