Integrated watershed management： evolution, development and emerging trends
Bibliographic record
Abstract
Watershed management is an ever-evolving practice involving the management of land,water,biota,and other resources in a defined area for ecological,social,and economic purposes.In this paper,we explore the following questions:How has watershed management evolved? What new tools are available and how can they be integrated into sustainable watershed management? To address these questions,we discuss the process of developing integrated watershed management strategies for sustainable management through the incorporation of adaptive management techniques and traditional ecological knowledge.We address the numerous benefits from integration across disciplines and jurisdictional boundaries,as well as the incorporation of technological advancements,such as remote sensing,GIS,big data,and multi-level social-ecological systems analysis,into watershed management strategies.We use three case studies from China,Europe,and Canada to review the success and failure of integrated watershed management in addressing different ecological,social,and economic dilemmas in geographically diverse locations.Although progress has been made in watershed management strategies,there are still numerous issues impeding successful management outcomes;many of which can be remedied through holistic management approaches,incorporation of cutting-edge science and technology,and cross-jurisdictional coordination.We conclude by highlighting that future watershed management will need to account for climate change impacts by employing technological advancements and holistic,cross-disciplinary approaches to ensure watersheds continue to serve their ecological,social,and economic functions.We present three case studies in this paper as a valuable resource for scientists,resource managers,government agencies,and other stakeholders aiming to improve integrated watershed management strategies and more efficiently and successfully achieve ecological and socio-economic management objectives.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".