Agriculture in a Water-Scarce World
Bibliographic record
Abstract
With a relatively small population and 7% of the world's available freshwater resources, Canada is well placed for a world of water scarcity where the real value of water in its many uses becomes more and more apparent. However, action is necessary to ensure that Canada continues to benefit from the social, economic and environmental goods and services derived from water resources. Experience and analysis suggests that policy and incentives play critical roles in the sustainable exploitation of natural resources. In particular, properly valuing water in all its forms and uses appears to be critical. Analysis abroad has underlined the benefits of clearly delineating the roles of regulators, resource managers, infrastructure operators and service providers. The separation of water property rights and use rights from land title issues has also been found to improve incentives and resource governance. Approaches which build in rewards for non-market benefits and penalties for negative spill-overs have achieved success. To better prepare for the future, Canada' s water governance institutions need to explore means of improving our own water allocation and incentive systems. Experts in the field have already identified several areas where effort is warranted: plant breeding to deal with water scarcity and changing climate in areas of stress; understanding and better protecting natural capital and ecosystems that will become scarcer in future; inter-agency collaboration to ensure coordinated engagement on water with U.S, and; undertaking more comprehensive bio-economic modeling and analysis to better anticipate water stresses at home and abroad.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".