Spatially variable effects of streamflow on water temperature and thermal sensitivity within a salmon‐bearing watershed in interior British Columbia, Canada
Bibliographic record
Abstract
Abstract Warming temperatures can have negative consequences for aquatic organisms, especially cold‐adapted fishes such as Pacific salmon. The magnitude of warming is related to the thermal sensitivity of streams in salmon‐bearing watersheds (i.e., change in stream temperature for every 1°C increase in air temperature), which can vary based on several factors including streamflow. Management actions to increase streamflow may therefore benefit salmon by decreasing thermal sensitivity. However, the effects of streamflow on thermal sensitivity are often complex, as the temperature of flow inputs can directly increase or decrease temperatures. This study aimed to disentangle the influence of streamflow on thermal sensitivity and stream temperature over 4 years in the Nicola River, a regulated semiarid watershed in south‐central British Columbia, Canada. A statistical modeling approach was used to estimate streamflow effects on stream temperatures and thermal sensitivity (i.e., relationship of regional air temperature to stream temperature) at 12 sites from 2018 to 2021. Streamflow had a variable influence on stream temperatures across the watershed via both direct effects and by modulating thermal sensitivity. At a given site, streamflow was generally negatively associated with summer daily mean stream temperature, but the magnitude of its influence varied among locations and years. The influence of streamflow on thermal sensitivity was also highly variable both spatially and temporally. The analysis suggests that there may be complex relationships between streamflow, stream temperature, and thermal sensitivity, which complicates the efficacy of flow as a lever to mitigate high temperatures in regulated systems.
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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 | 0.000 |
| 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".