Hydrodynamic and physical assessment of ice-covered conditions for three reaches of the Athabasca River, Alberta, Canada
Bibliographic record
Abstract
Water is needed for oil sand developments in the lower Athabasca River basin of northern Alberta, Canada, and is also a key consideration from an ecological and fish habitat perspective, particularly in winter when river flows are at their lowest. Efforts to establish an appropriate flow management regime for the lower Athabasca included revision of River2D, a fixed bed, depth-averaged finite element model, available from www.river2d.ca, to predict hydraulics with a partial or total ice cover. Hydrometric surveys from three reaches of the Athabasca River were used to test the model, assess different model calibration methods, and simulate hydrodynamics for ice-covered conditions. Calibrating bed roughness from ice-free data, assuming the same bed roughness for ice-covered conditions, or the reverse, provided a close fit to the surveyed water surface elevations. The applied ice and composite roughness heights differed according to the applied method of calibration. This may have implications for local velocity estimates possibly affecting fish habitat suitability. A range of bed and composite grain roughness heights, corresponding to different bed substrates and ratios of bed and composite roughness heights to water depths, are provided for model calibration purposes. Key words: hydrodynamics, hydraulics, ice, ecology, environment, fish habitat, winter, oil sands.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".