Hydro‐thermal modelling of the potential impacts of reservoirs on water temperature and incubation time of Atlantic salmon and brook trout in the Tobique River, Canada
Bibliographic record
Abstract
Abstract Water temperature is recognised as a crucial variable in lotic ecosystems affecting the metabolism of aquatic organisms, with extremely high‐temperature events increasing the risk of mortality of various species. Impounding rivers to regulate flow and generate electricity is one of the causes of altered thermal regimes in river systems. This paper presents the results of a study to simulate the impact of reservoirs on downstream temperatures of the Tobique River, New Brunswick, Canada. CEQUEAU, a hydrological and water temperature model, was used to simulate and assess the flow and water temperature of the Tobique River from 1997 to 2020. It was coupled to a statistical model to generate water temperature at different depths in the reservoir. Results show that the CEQUEAU model was successfully calibrated for water temperature with an overall root mean square error of 1.7°C. Based on the results, when water is drawn from the lower parts of the reservoir, the mean water temperature downstream of the dam increases in winter and spring by ~1°C. The results of this paper provide insight into how dams can affect the incubation time of cold‐water species in eastern Canada. We calculated the hatching degree days of Atlantic salmon and Brook trout to evaluate the impact of dams on fry emergence. Results indicated that the hatching period of Atlantic salmon and Brook trout downstream of reservoirs may shorten under the influence of dams.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".