Augmenting environmental flow information with water temperature: case study in Eastern Canada
Bibliographic record
Abstract
The increasing global water demand and climate change put freshwater resources and riverine ecosystems at risk of increasing scarcity and conflict in water usage. Stream biota may be confronted with increasing stressful aquatic habitat conditions due in part to increasing water temperatures. In response to these issues, environmental flows play a crucial role in flow assessment, water resource management and the protection of aquatic biota. Environmental flows (eflows), also known as instream flow requirements, refer to the amount of water needed in rivers to maintain a balanced aquatic ecosystem. Recently, the inclusion of river temperature in the assessment of eflows has raised interest, especially in the context of climate change and dam operations, which are altering the river thermal regimes and affecting aquatic habitat. This study focuses on hydrological metrics that can be used to prescribe eflows in Atlantic Canada and Quebec (Eastern Canada). Eflow analyses were conducted jointly with the analyses of river temperatures at 61 sites. The results show that summer environmental flow metrics can be associated with relatively high water temperatures during a period when water withdrawals may be important. Classifying rivers according to their thermal regime during summer low flow periods prior to prescribing an eflow target is therefore recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".