Characteristics of Integrated Water Resource Management in the Zambezi River and Great Lakes Basins: A Comparison of Two Approaches
Bibliographic record
Abstract
Integrated Water Resources Management (IWRM) is evolving as a contemporary means to address complex and critical issues associated with making the most effective and efficient use of water resources. Water resource challenges in the Zambezi River Basin include both quality and quantity issues including potential diversions from the basin to localities outside the basin and lack of an agreed upon institutional framework for the management of the Zambezi River system. In 1972, the United States and Canada signed the first Great Lakes Water Quality Agreement. This agreement committed the two countries who share the trans-boundary waters of the Great Lakes to restore and enhance water quality in the Great Lakes System. Amendments in 1987 resulted in establishing the goal to virtually eliminate persistent toxic substances into the Great Lakes resulting from human activities. In 2008, the Great Lakes Compact was approved by all of the eight Great Lakes States plus the Provinces of Ontario and Quebec. This compact was subsequently approved by the Congress of the United States and signed by President Bush on October 3, 2008. Both the Zambezi River Basin and the Great Lakes Basin offer valuable insights into the application of IWRM to critical water resource planning and management challenges in their respective geographical locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".