Existing Opportunities to Adapt the Rio Grande/Bravo Basin Water Resources Allocation Framework
Bibliographic record
Abstract
The study of the Rio Grande/Bravo (RGB) Basin water allocation demonstrates how the United States (U.S.) and Mexico have consolidated a transboundary framework based on water sharing. However, the water supply no longer meets the ever-increasing demand for water or the expectations of different stakeholders. This paper explores opportunities for an enhanced management regime that will address past problems and better examine how to balance demands for a precious resource and environmental needs. Based on an overview of the RGB Basin context and the water allocation framework, as well as a discussion on stakeholders’ ability to achieve solutions, this paper explores three key questions: (1) Does the current binational water allocation framework meet current and future human and environmental needs? (2) How can the U.S.-Mexico water allocation framework be adapted to balance social and environmental water demands so it can support and preserve the RGB Basin ecosystem? (3) What are the main opportunities to be explored for expanding the U.S.-Mexico water resources allocation framework? The U.S.-Mexico water resources framework is subject to broad interpretation and may be adapted to the circumstances taking the fullest advantage of its flexibility. Policy recommendations highlight the existing flexibility of the binational framework, the potential to move forward with an ad hoc institutional arrangement, and the creation of political will to achieve change through stakeholders recommendations.
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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 | 0.009 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".