Water resource allocation in Canada (Manitoba) and Brazil (Ceara), legal and institutional impacts on Bulk Water Removal
Bibliographic record
Abstract
This thesis presents a comparative analysis of water allocation systems and their legal and institutional impacts on Bulk Water Removal (BWR), based on Canadian (Manitoba) and Brazilian (Ceara) systems. First, it studies the BWR concept, opportunities and problems, federal-provincial jurisdiction, international issues and management duties. Then, it analyses the water allocation issues that contribute to water shortage and needs for BWR. This thesis argues that legal frameworks as well as policies can contribute to scarcity and the need for water transfer. Current water allocation regimes are not effective in dealing with water scarcity and, in fact, tend to exacerbate the problems experienced in the two regions studied. Thus, either a simple BWR moratorium or a non-assessed and non-monitored BW is an unsustainable solution to water scarcity issues. This thesis concludes its analysis by offering suggestions for future water allocation systems, which include a legally well-defined water rights concept, participative and decentralised water management and an integrated legal strategy to establish adaptable allocation mechanisms. This will answer current and potential water demands and serve to avoid future water shortages, conflicts and needs for BWR.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".