Alberta Water Resources, Policies, Legislation and Goals: The Quest to Awaken "Sleeper Rights"
Bibliographic record
Abstract
Water is arguably the most critical natural resource to Alberta’s future. The quantity and quality of water will shape the social, economic, and environmental dimensions of Alberta’s future. The quality of life in Alberta will depend on our ability to allocate this finite resource in both an efficient and environmentally responsible manner. The issue addressed in this research is how Alberta’s current water policies manage “sleeper rights” and why these policies need to be updated. Sleeper rights describe water licenses that are allocated to a water user but are not fully utilized. This allocated but under-utilized water is important because it helps Alberta’s major watersheds to meet its instream flow needs (IFNs). IFNs refer to the amount of water that aquatic ecosystems require to provide Albertans with safe and secure drinking water; healthy aquatic ecosystems; and reliable quality water supplies. By the end of 2005, the Alberta Environment and Sustainable Resource Development (AESRD) had allocated approximately 9.5 billion cubic metres of water throughout Alberta. By the end of 2010, this had increased to 9.9 billion cubic metres. The three sectors representing the highest water demands and allocations in Alberta are the agricultural sector (44.3%), commercial sector (29.5%), and municipal/ domestic sector (11.3%). However, not all of these allocations are fully utilized. By some estimates, as much as 45 percent of water allocated under license in Alberta remains unused.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".