Applying a two‐dimensional hydrodynamic model to estimate fish stranding risk downstream from a hydropeaking hydroelectric station
Bibliographic record
Abstract
Abstract Fish stranding is of global concern with increasing hydropower operations using hydropeaking to respond to fluctuating energy demand. Determining the effects hydropeaking has on fish communities is challenging because fish stranding is dependent on riverscape features, such as topography, bathymetry and substrate. By using a combination of physical habitat assessments, hydrodynamic modelling and empirical data on fish stranding, we estimated the number of fish stranding over a 5‐month period for three model years in a large Prairie river. More specifically, we modelled how many fish potentially stranded during the years 2019, 2020 and 2021 across a 16 km study reached downstream from E.B. Campbell Hydroelectric Station on the Saskatchewan River, Canada. Fish stranding densities calculated from data collected through remote photography and transect monitoring in 2021 were applied to the daily area subject to drying determined by the River2D hydrodynamic model. The cumulative area subject to change was 90.05, 53.02 and 80.74 km 2 for years 2019, 2020 and 2021, respectively, from June to October. The highest number of stranded fish was estimated for the year 2021, where estimates ranged from 89,800 to 1,638,000 individuals based on remote photography and transect monitoring fish stranding densities, respectively, 157 to 2,856 fish stranded per hectare. Our approach of estimating fish stranding on a large scale allows for a greater understanding of the impact hydropeaking has on fish communities and can be applied to other riverscapes threatened by hydropeaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".