AGENDA: Coping with Water Scarcity in River Basins Worldwide: Lessons Learned from Shared Experiences
Bibliographic record
Abstract
Water scarcity is increasingly dominating headlines throughout the world. In the southwestern USA, the looming water shortages on the Colorado River system and the unprecedented drought in California are garnering the greatest attention. Similar stories of scarcity and crisis can be found across the globe, suggesting an opportunity for sharing lessons and innovations. For example, the Colorado River and Australia's Murray-Darling Basin likely can share many lessons, as both systems were over-allocated, feature multiple jurisdictions, face similar climatic risks and drought stresses, and struggle to balance human demands with environmental needs. In this conference we cast our net broadly, exploring several salient topics including: trans-boundary cooperation, water marketing, Indigenous water rights, environmental and social water needs, and drought coping. This public event will be informed by three invitation-only meetings held immediately before the conference: an “Indigenous Water Justice” symposium; a “Social Dimensions of Environmental Water Management” workshop; and a “Drought Crises in Federations” symposium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".