Performance evaluation of a tailings pond seepage collection system
Bibliographic record
Abstract
Disposal of oil sands tailings in ponds is a common method used by oil sands operators to manage the large volume of tailings generated from oil sands mining. This study considered a large tailings pond with an 11 km long ring dyke that was constructed of permeable tailings sand and equipped with drains and seepage collection ditches designed to collect process-affected water (PAW) from the dyke. The effectiveness of this seepage collection system was examined at the downgradient end of the tailings pond and dyke system using a focussed field investigation supported by groundwater flow modelling. A network of piezometers and drive points were installed in a 1 km2 area to facilitate hydraulic measurements and water sampling to characterize the surface water and groundwater flow system. Chemical tracers suggest migration of PAW in a shallow, permeable sand deposit beyond an inner seepage collection ditch, but elevated hydraulic heads beyond the outer ditch have prevented further migration. A groundwater flow model was used to simulate the observed hydraulic dyke conditions and estimate the amount of PAW discharging into the shallow aquifer in the study area. Under the present hydraulic conditions, the seepage collection system is currently working to effectively contain PAW.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".