Land Use and Water Quality Relationships in the Lower Little Bow River Watershed, Alberta, Canada
Bibliographic record
Abstract
Abstract Water quality in the Lower Little Bow River was monitored to determine if irrigation return flow streams had a significant impact on river water quality and to examine relationships between land use and water quality in this diverse agricultural watershed. Water samples were collected weekly or biweekly during the irrigation season and monthly in winter for three years. A comprehensive land use assessment was also completed. Significant differences in flows, and in nutrient and bacteria loads, were found along the mainstem of the river following the inflows of irrigation return water; however, differences in concentrations were only significant in a drought year when mainstem flows were reduced. Pearson correlations among land use, soil types, and water quality variables identified significant positive relationships between the proportion of cereals, irrigated land, and confined feeding operation (CFO) density and maximum concentrations of total nitrogen (TN), nitrate-nitrogen, and total phosphorus (TP) that were observed during runoff events. Most nutrient variables were inversely related to the proportion of native prairie. The variation in maximum TP and median dissolved P concentrations was largely explained by the proportion of cereals in the sub-basin, while the variation in maximum and median TN concentrations was explained by the proportions of irrigated land and native prairie, respectively. Microbiological variables were not related to any of the measured variables, suggesting that factors influencing bacteria populations operate at different scales.
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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 | 0.002 |
| 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".