Managing Drought Risk in the Okanagan: A Role for Dry-Year Option Contracts?
Bibliographic record
Abstract
Although markets have proven effective at moving scarce water to high-value uses, the potential economic efficiency gains are often insufficient to convince water users to accept more market-oriented water allocation mechanisms. Water users in the semi-arid areas of British Columbia, including the Okanagan basin, are deeply concerned about the potential negative impacts of water markets. With drought risk rather than chronic water scarcity as the primary water management challenge, water users' concerns are reasonable. Dry-year option contracts may capture some of the benefits of market-based instruments without the risk implications of conventional water markets. Dry-year water option contracts are financial instruments that water-sensitive users can use to hedge themselves against drought impacts, and if appropriately managed through a water supplier, they may be an efficiency-enhancing alternative to crop insurance. We argue that with appropriate support, dry-year option contracts can be a useful low-cost tool to mitigate drought impacts in semi-arid regions of British Columbia. Such contracts may enable the innovative local water management solutions envisioned in British Columbia's recently enacted Water Sustainability Act.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".