Soil test phosphorus and nitrate adjacent to artificial and natural cattle watering sites in southern Alberta
Bibliographic record
Abstract
Off-stream watering troughs may reduce surface water pollution by keeping nutrients away from natural water bodies, but may increase nutrient contaminant of groundwater. The objective of this study was to determine to what extent off-stream watering troughs active for 2 to 7 yr caused enrichment and leaching of soil test P (STP) and KCl-extractable NO3-N. The study was conducted in the Lower Little Bow (LLB) River watershed of southern Alberta, Canada. Soil samples were obtained at three recently installed off-stream watering troughs, four active cattle watering sites adjacent to the LLB River, and at two sites along a fenced reach of the river with no cattle access. At each location, samples were obtained along four 100-m transects. Surface (0-5 cm) soil immediately adjacent to the LLB River was not enriched in STP or NO3-N, which was attributed to flushing of nutrients during periods of high flow. Surface soil at distances ≤ 5 m from the three water troughs was approximately three times higher in STP than surface soil obtained at distances ≥10 m and was seven times higher in NO3-N. Subsurface soil layers adjacent (3 m distance) to the three water troughs were not enriched in STP compared with background levels (100 m distance). The subsurface soil adjacent (3 m) to the longest active watering trough was enriched in NO3-N to the 60 cm depth compared with background levels (100 m). Greater nutrient enrichment at the off-stream watering troughs than at the cattle watering sites adjacent to the river suggested that this beneficial management practice (BMP) was effective in shifting nutrient distribution away from the river. Key words: Pasture, nutrient leaching, nitrate, best management practices
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".