Influence of streambank fencing on vegetation and soil of the Mixed prairie component in a complex corridor pasture
Bibliographic record
Abstract
A 5 yr (2011–2015) field study was conducted to test the hypothesis that streambank fencing had a significant effect on selected vegetation and soil properties of the Mixed prairie component of a complex corridor pasture. The grazing treatments [ungrazed (UG) – periodic grazing (PG)] inside the corridor pasture were 11 yr (2001–2012) of cattle exclusion (UG), followed by 3 yr (2013–2015) of periodic grazing (PG) when the riparian soil was dry. A control treatment outside the fencing was continuous grazing (CG). Selected vegetation and soil properties were measured over the growing season at 10 paired locations in each treatment (nonreplicated) pasture over 5 yr (2011–2015), and rangeland health was measured in 2011. The UG–PG treatment significantly (P ≤ 0.10) increased the total biomass by 2- to 5-fold in all 5 yr compared with CG treatment and improved the rangeland health score of the UG phase of the UG–PG (63%) treatment compared with the CG treatment (50%) in 2011. It also significantly reduced surface soil temperature by 2.2–5.2 °C, significantly increased volumetric water content of the surface soil by 7%–10% in 3 of 5 yr, and significantly increased surface soil CO2 efflux (instantaneous) by 17%–60% in all 5 yr. Overall, the UG–PG treatment improved rangeland health, increased total biomass, soil water, and soil CO2 efflux of the Mixed prairie, but decreased soil temperature compared with the CG treatment. Excessive dead biomass, greater fire risk, and an increase in noxious weeds caused by cattle exclusion suggested that periodic grazing may be the preferred option.
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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 | 0.001 | 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 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".