Influence of Streambank Fencing on the Environmental Quality of Cattle‐Excluded Pastures
Bibliographic record
Abstract
Limited information exists on the effect of streambank fencing on riparian zone pastures. The objective of this study was to test the hypothesis that 4 to 6 yr of streambank fencing would improve the environmental quality of the cattle-excluded pasture compared with the grazed pasture and cause the fenced pasture to act as a buffer or filter strip. Rangeland health, vegetative and soil properties, and rainfall simulation runoff were measured in the cattle-excluded and adjacent grazed native pastures along the fenced reach of the Lower Little Bow River in southern Alberta, Canada, for 3 yr (2005-2007). Rangeland health was improved (health score increase from 55 to 72%); vegetation cover (13-21%) and standing litter (38-742%) were increased; and bare soil (72-93%) and soil bulk density (6-8%) were decreased under cattle exclusion, indicating an improvement in environmental quality from streambank fencing. In contrast, other vegetation (total and live basal area, fallen litter) and soil properties (soil water and soil C, N, and P) were not improved by cattle exclusion. Cattle exclusion significantly (P </= 0.10) reduced surface runoff depth of water (21-32%) and mass loads of total N fractions (21-52%) in 2 of 3 yr compared with the grazed pasture, suggesting that this fenced pasture may act as a buffer for certain runoff variables. In contrast, other runoff variables (turbidity, electrical conductivity, pH, concentrations and loads of total suspended solids, and certain N and P fractions) in the cattle-excluded pasture were generally not improved by streambank fencing. Overall, streambank fencing improved the quality of certain environmental variables within the cattle-excluded pasture.
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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 | 0.000 | 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".