Alberta‘s Water for Life Strategy: Some early indications of its acceptance by the irrigation industry in Southern Alberta.‖ [Paper presented at the 2006 Annual Meeting of the Canadian Agricultural
Bibliographic record
Abstract
Water is essential for sustainable agricultural development – for irrigation of crops, livestock watering, processing, and sustaining farm families. Agriculture uses 71 percent of all water diverted for consumptive use in Canada (Environment Canada, 2004), and is by far the greatest water consumer in Canada. In the absence of a Canadian national water strategy, Alberta has developed a long-term water management plan called the Water for Life Strategy. Its successful implementation will depend largely on the participation of irrigators. This study explores the reaction of irrigators to one of the strategy’s main goals – a 30 percent increase in water use efficiency and productivity by 2015 over 2005 levels. The study reveals that irrigators vary significantly in their views as to the extent to which this goal can be reached, and the means by which it should be achieved within agriculture. Further, these responses reflect differences among irrigation districts relating to the extent of water stress, on-farm irrigation water efficiency and natural factors that limit crop diversity in some areas. Ultimately the government may have to revise its 30 percent target and tailor the strategy to irrigation districts as opposed to a broad-based approach.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".