The water and energy balance of Lake Miwasin: a pilot-scale oil sands pit lake
Bibliographic record
Abstract
Energy companies in the Athabasca Oil Sands Region in Alberta, Canada are studying the viability of incorporating pit lakes into reclamation closure designs to both sequester tailings and re-integrate the mining lease into the broader natural landscape. Lake Miwasin is a pilot-scale oil sands pit lake encompassed by a constructed catchment where the volume of the water cap is not actively managed. This research quantified the water and energy balances of Lake Miwasin during the open water season for two consecutive years. As the constructed catchment lacks both natural waterbodies and connectivity to a legacy groundwater system, freshwater additions to the lake during the summer season were governed by rainfall. Above average rainfall during the first year triggered surface water inflow events that diluted the over-winter water volume by ~ 25%. The second year had below average rainfall resulting in minimal surface water inflow and a 30 cm drop in lake stage. The lake became thermally stratified during the open water season absorbing high amounts of energy in the spring and releasing this energy in late summer/fall. Despite being constructed at a pilot-scale, the timing and magnitude of the maximum heat content were comparable to small natural waterbodies. The small fetch and surrounding landscape features led to a sheltering effect reducing wind action at the surface contributing to lower correlations between climatic variables and the surface energy fluxes compared to larger neighboring lakes. This research indicates variable climatic conditions, lake size, and surrounding landscape features will influence the water balance and energetics of future oil sands pit lakes. Consideration of the presented results and continued research is required to guide the implementation of these contemporary landscape features throughout the Athabasca Oil Sands Region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".