Managing water resources in a time of global change: mountains, valleys and flood plains
Bibliographic record
Abstract
Series editor's preface, Henry Vaux, Jr, 1. High Water, Low Water, No Water: Upland Water Resource Management in times of Global Change, Ariel Dinar and Alberto Garrido, Part I. Mountain and upland areas, 2. Mountains of the World - Water Towers for the 21st Century, Bruno Messerli, Daniel Viviroli and Rolf Weingartner, 3. Upland Watershed Management and Global Change - Canada's Rocky Mountains and Western Plains, Dave Sauchyn, Michael Demuth and Alain Pietroniro, 4. System Hydrology Tools To Support Water Policy: The Case Of Lake Kinneret Watershed, Israel, Alon Rimmer, 5. Combined impacts of development and climate change on the Rhone river (Switzerland, France), Jean-Paul Bravard, Part II. Managing Opportunities in the face global challenges, 6. Adaptive Management of Upland Rivers Facing Global Change: General Insights and Specific Considerations for the Rhone, Claudia Pahl-Wostl, Ger Berkamp and Katharine Cross, 7. Climate Change and Water Resources: Potential Impacts and Implications, Richard M. Adams and Dannele E. Peck, 8. Policy and Adaptation in the Jordan Valley, Yousef Hasan AYADI, 9. Upland Watershed Management in Developing World: The Case of Tunisia, Sihem Benabdallah, 10. Challenges In Upland Watershed Management: The Green-Blue Water Approach, Holger Hoff, Part III. The view of practitioners and stakeholders, 11. Factors for success: Public and Private Sector Roles in Securing a Safe and Reliable Water Supply, Don Lowry, 12. The International Joint Commission: A Case Study in the Management of International Waters, Dennis Schornack and John Nevin, 13. An overview of the IJC, Jack Blaney, 14. Water management in alpine regions, Pedro Arrojo-Agudo, Conclusions, 15. Overcoming the Constraints for a more Integrated and Adaptive Water Management: a New Agenda for Upland Waters , Alberto Garrido and Ariel Dinar.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".