Water conveyance and on-farm irrigation system efficiency gains in southern Alberta irrigation districts from 1999 to 2012
Bibliographic record
Abstract
Efficiency gains from on-farm irrigation system upgrades and canal rehabilitation in southern Alberta irrigation districts are influenced by weather variability. The Irrigation Demand Model was used to estimate differences in on-farm demand and conveyance losses based on irrigation district characteristics in 1999 and 2012 using weather data from 1928 to 2012. Monte Carlo simulations were subsequently performed to determine the magnitude of potential efficiency gains at different chances of exceedance. Changes in irrigation systems and water conveyance infrastructure reduced gross demand by 74 mm from 1999 to 2012, with a 55-mm reduction in on-farm demand and a 19-mm decrease in conveyance losses at a 10% chance of exceedance. Reductions in gross demand on a volume basis from 1999 to 2012 ranged from 170 to 200 million m3, even with about 30,300 ha of irrigation expansion. Conveyance loss reductions were stable at about 50 million m3, so 70 to 75% of the potential water savings were achieved through reduced on-farm demand. Mean seasonal naturalized flows available for use in southern Alberta from 1912 to 2009 ranged from 2.08 billion m3 in high-demand years to 3.95 billion m3 in wet years. Gross demand based on irrigation district characteristics in 2012 varied from 1.73 billion m3 in wet years to 2.83 billion m3 in high-demand years. Additional gains in efficiency from on-farm irrigation system upgrades and rehabilitation of conveyance infrastructure in the future will help mitigate the increased risk of water scarcity as irrigation districts expand with current licensed water allocations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".