Trend analysis of water quality in the irrigation districts of southern Alberta, Canada
Bibliographic record
Abstract
Abstract Irrigation is essential for high agricultural production and crop diversity in semi‐arid regions such as southern Alberta, Canada. Nearly 75% of Canada's irrigation takes place in Alberta's irrigation districts, making the management and maintenance of irrigation water quality highly important in this region. In this study, temporal water quality trend analysis was conducted on southern Alberta irrigation water from 2006 to 2023. The trends of 19 water quality parameters, including nutrients, salinity, physical characteristics, Escherichia coli , and pesticides, were evaluated at 74 sites. Of the total number of parameter‐by‐site tests (902) conducted, 36.5% had statistically significant decreasing trends, 1.3% had statistically significant increasing trends, and 62.2% had no trend. This indicates stability and improvement in water quality during the study period at the resolution of individual sites. Regional trend analysis revealed trends in 11 out of the 19 parameters tested: 9 decreased, 2 increased, and 8 exhibited no trend, which also indicated stable and improving water quality. Continued monitoring is important for areas and parameters showing increasing trends to guide mitigation action. This information can be used to focus water and land management decisions and direct resources to priority areas and parameters to ensure excellent quality irrigation water for all users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".