Influence of mowing and narrow grass buffer widths on reductions in sediment, nutrients, and bacteria in surface runoff
Bibliographic record
Abstract
Miller, J. J., Curtis, T., Chanasyk, D. S. and Reedyk, S. 2015. Influence of mowing and narrow grass buffer widths on reductions in sediment, nutrients, and bacteria in surface runoff. Can. J. Soil Sci. 95: 139-151. Little research has been conducted on the effect of mowing and buffer width on the effectiveness of short-width (< 10 m) native grass buffers to filter sediment, nutrients, and bacteria. A 2-yr (2011-2012) field study was conducted on native rangeland in southern Alberta. The treatments of mowing and buffer width (1.5, 3, 6 m) were evaluated using a randomized complete block design with four replicates. The buffer plots were pre-wet with distilled water. A spiked solution was then applied to each plot using a run-on distribution device and the runoff collected every 10 min for 30 min once the runoff started discharging from the plot. The volume of runoff, and percent reduction in concentration and mass of sediment [total dissolved solids (TSS)], phosphorus [dissolved reactive P (DRP), total P], nitrogen (total N), and bacteria (Escherichia coli, total coliforms) in runoff were determined. The findings did not support our hypothesis that percent reductions in concentrations and mass for sediment, nutrients, and bacteria were greater for mowed than unmowed buffers. In contrast, the findings supported our hypothesis that increasing buffer width would significantly (P≤0.05) decrease mass (but not concentration) of sediment, nutrients, and bacteria in runoff. The significant mass reduction was attributed to a reduced runoff ratio caused by longer residence time and greater infiltration in the wider buffers. Mass reductions for the three buffer widths ranged from 29 to 92% for TSS, 22 to 93% for DRP, 38 to 93% for total P, 23 to 92% for total N, and between 61 and 94% for E. coli and total coliforms. These findings suggest that buffer width but not mowing may reduce runoff quantity and improve runoff quality over the short term.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".