Trade-Offs between Human and Environment: Challenges for Regional Water Management under Changing Conditions
Bibliographic record
Abstract
Water resource systems are under unprecedented pressure mainly due to rapid socio-economic growth, weak water and land management decisions, as well as variability and change in climate conditions. These pressures have disrupted the functionality of freshwater ecosystems and have generated water management challenges in various regions across the globe. Here, we showcase the potential trade-offs in the Province of Saskatchewan, Canada, between upstream human activities and downstream environmental needs under changing water availability conditions. We showed that an increase in irrigation areas can boost provincial economy but alter timing, magnitude and rhythmicity of the peak flows reaching downstream ecosystems. This indicates that the business as usual management might not be able to handle such emerging challenges. To improve water management, we argue that there is a need to better represent the dynamic interactions between human water use and water quantity and quality conditions and their influence on ecosystems. In addition, impact assessment frameworks need to be improved to better identify system vulnerabilities under changing natural and anthropogenic conditions. Moreover, due to the key role of stakeholders in adopting land and water management decisions, their viewpoints need to be understood and included in management decisions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".