Geometric shape, water availability and under ice volume of Alberta lakes
Bibliographic record
Abstract
Water availability information can be vital to the execution of informed management decisions. Since only a small fraction of Alberta lakes have surveyed bathymetry data, accurate estimation of lake water availability is often challenging. In this study, we analyzed available bathymetry data from 77 lakes, distributed over six major river basins and five natural regions of Alberta, and developed dimensionless relationships between volume and depth. We compared these relationships with the analytical relationship between volume and depth for five idealized lake shapes, viz. as cylindrical, pseudo-parabolic, parabolic, conic, and inverse-parabolic. Our study shows that considering the volume-depth relationship, 48% of Alberta lakes fall under parabolic shape, 29% fall under conic shape, and the rest (23%) fall under either pseudo-parabolic, inverse-parabolic, or cylindrical shape. We also developed four different models to estimate maximum lake volume (a proxy of lake water availability) and 5% under ice volume (a proxy for winter allocation limit of lake water) assuming an ice thickness of 80 cm. These models have been developed in such a way that allows the user to apply the models based on data availability and can be used in absence of site-specific data (e.g., bathymetry) to estimate volume, and subsequently water availability in lakes. Finally, we propose a formulation of percent volume reduction due to small water withdrawal, which requires only maximum depth of a lake to estimate a potential volume reduction limit for a water withdrawal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".