Water Management on the U.S.-Mexico Border: Achieving Water Sustainability and Resilience through Cross-Border Cooperation
Bibliographic record
Abstract
Shortly after being confirmed by the U.S. Senate in 2021, Commissioner Maria Elena Giner called for input into issues of importance to the U.S. Section of the International Boundary and Water Commission (USIBWC). Responding to her call, a group of border scholars committed to producing a white paper entitled “Water Management on the U.S.-Mexico Border: Achieving Water Sustainability and Resilience through Cross-Border Cooperation”. This document was presented to Commissioner Giner at the spring 2022 ABS Annual Meeting in Denver, Colorado. This commentary outlines the main ideas and recommendations in this white paper, which are intended to strengthen the USIBWC's ability to respond to the challenges of U.S.-Mexico border water management in the 21st century. The paper recognizes the IBWC's long history of handling binational water issues effectively and its demonstrated capacity to respond and adapt to the border region's changing social, political, and environmental conditions. The commentary is capped with Commissioner Giner's response to the white paper, including her commitment to work with the academic community in both countries in creating an IBWC's binational science advisory group, as recommended in the white paper.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".