Modeling water quality in an oil sands compensation lake
Bibliographic record
Abstract
Surface mining in the oil sands region of Alberta, Canada, often requires that mining operators drain lakes or divert streams to access the underlying ore. “Compensation lakes” can be constructed to create new fish habitat to offset the loss of fish habitat due to mining activity and to satisfy conditions under a project’s Fisheries Act Authorization. The design of these lakes requires prediction of future water temperature and dissolved oxygen levels to determine the suitability of the new habitat for fish. These predictions are made using a calibrated hydrodynamic and water quality model. Until recently, there were not any built compensation lakes in the region with enough measured water quality data that could be used to calibrate such a model. This paper uses measured data from Horizon Lake, a recently built compensation lake, to calibrate Generalized Environmental Modeling System of Surfacewaters (GEMSS), a three-dimensional hydrodynamic and water quality model, used to model the lake. Horizon Lake was built in 2008 by Canadian Natural Resources Ltd. and water quality in the lake has been monitored for the last seven years. The results of the model calibration to observed water temperature and dissolved oxygen provide rates and coefficients, notably sediment oxygen demand, that can be used to improve model applications to other planned compensation lakes.
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 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".