Predicted effects of flow diversion by <scp>Run‐of‐River</scp> hydropower on bypassed stream temperature and bioenergetics of salmonid fishes
Bibliographic record
Abstract
Abstract Many anthropogenic disturbances impact stream ecosystems by changing flow and temperature regimes. The emerging industry of small Run‐of‐River (RoR) hydropower reduces streamflow in bypassed reaches, with largely unknown consequences for water temperatures and fish growth. We used empirical and simulated data from two small RoR regulated streams in British Columbia (Canada) to quantify changes in water temperatures in bypassed reaches and assess the potential impacts to resident rainbow trout ( Oncorhynchus mykiss ) growth using bioenergetics models under a range of consumption scenarios. We found increases in mean monthly water temperature in bypassed reaches due to flow diversion of 0.5–0.8°C (0.17–0.19°C/km). Bioenergetics models using those temperatures predicted increases in annual O. mykiss growth (compared to natural temperatures) if consumption was unlimited (+200–450%), increases (+15–42%) if consumption was scaled with higher metabolic demand, and small reductions (−5 to 7%) if consumption remained constant. If food availability was reduced by 25%, annual growth was predicted to decline by 45%. Empirical estimates of annual growth of fish sampled indicate modest reductions in annual growth less severe than those modelled by our Scenario 2. Our results highlight that increases in water temperature induced by flow diversion for small RoR hydropower could be large enough to have consequences for O. mykiss growth, but the impacts depend on how and when RoR hydropower affects food supply and consumption.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".