Irrigation’s Impact on Economic Growth in Alberta, Canada
Bibliographic record
Abstract
Irrigation development in semi-arid regions provides benefits for producers as well as many others who reside in the region. Although a common perception exists that irrigation benefits only irrigation producers, a study carried out by Paterson Earth and Water Consulting Ltd showed that irrigation positively impacts many more sectors of the Alberta economy than just irrigation producers. Compared to dry land (rain fed) agriculture, irrigation creates increased employment and economic activity through the purchase of additional farm inputs as well as processing of agricultural products. Multi-use water storage reservoirs, which support irrigation agriculture, provide societal benefits through recreation, hydropower generation, and water supply for habitat development, communities and industries. This study estimated that Alberta’s irrigation industry, which represents less than 5% of the cultivated land base, generates about $3.6 billion to the provincial gross domestic product (GDP), accounting for about 20% of the total agri-food sector GDP. It is also responsible for generating about $2.4 billion in income and creating about 56,000 jobs. Many of these jobs and incomes are generated in the rural regions of the province, and serves as an important part of the rural development initiatives in Alberta. Almost 90% of the GDP generated by the irrigation sector accrued to the region and the province, and only 10% accrued to irrigation producers.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".