Private Irrigators in Southern Alberta: A Survey of Their Adoption of Improved Irrigation Technologies and Management Practices
Bibliographic record
Abstract
Private irrigators account for about one-quarter of total irrigated area in southern Alberta with the balance of irrigation taking place within 13 irrigation districts. Some 1,367 private irrigation license holders exist in Alberta with a total irrigable area of more than 125,000 hectares. This group’s participation in achieving the province’s Water for Life objective of a 30 percent increase in water efficiency and productivity is important. Based on a random survey of private irrigators, it was found that they are grounded in family farm traditions, have been slow to adopt improved irrigation technologies and management practices in the past, and have even less intention of doing so in the future. A host of factors are causing this low adoption rate, but foremost are financial constraints and physical field conditions. Large cash subsidies or the establishment of additional processing facilities especially for Timothy hay and alfalfa would be required to motivate this group to adopt improved irrigation technologies. Adopting improved management practices may occur more readily since these would not involve large financial outlays. Water savings may be possible since the decision of when and how much to irrigate continues to be based on the long-standing tradition of eye-balling the crop’s condition and judging the weather.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".