The Oil Sands Pit Lake Model – Sediment Diagenesis Module
Bibliographic record
Abstract
In the Athabasca Oil Sands Region of Canada, bitumen is close enough to the surface and sufficiently concentrated to be recovered using conventional surface mining and chemical extraction.At the end of mining operations, oil sands mining operators have proposed to use pit lakes as closure waterbodies, and many of these pit lakes will contain water-capped tailings.The tailings pore water will contain various amounts of labile and refractory organic compounds, nutrients, trace metals and ion concentrations that ultimately will be released to the water cap as the tailings consolidate.In addition to advective release of constituents, chemical reactions within the tailings will result in changes to the redox state, speciation and phase of several chemicals.To predict the fate of various constituents in oil sands tailings, a sediment diagenesis module has been developed that is coupled with CE-QUAL-W2.The module includes several physical and chemical processes, such as tailings consolidation, coupled with lake bed deepening and pore water release; biogenic gas production (build up and release of methane, hydrogen sulphide and ammonia); physical release of bubbles through water column; unconsolidated sediment resuspension during bubble ebullition; dynamic oxygen consumption at lake bed (from sediments) and in water column (from bubbles); salt rejection during ice formation.This paper presents the model framework used to develop the Oil Sands Pit Lake Model sediment diagenesis module, including the basis for module algorithms, stage of development and potential uses for the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".