The effects of land use intensification on soil biodiversity in the pasture
Bibliographic record
Abstract
A long-term multidisciplinary study of pasture agroecology is currently being conducted in Nova Scotia, Canada. The objective is to examine the relationship between above -ground and below-ground functional diversity, and the effects on agroecosystem productivity. The experimental design consists of four treatments of decreasing land use intensity, applied in the context of management intensive grazing. Treatments include: (1) clipping and harrowing following each grazing rotation (intensive); (2) clipping only once following the first defoliation; (3) grazing every second rotation; (4) grazed only once a year (extensive). Samples were taken from each treatment once during May, July, and September 2005. Edaphic characteristics and plant diversity were measured, as well as microarthropod, nematode, protist, and bacterial functional group diversity and abundances. Significant (P < 0.05) effects of treatment were observed on percentage of bare soil, plant species and functional diversity, and bacterial functional diversity. There were also significant (P < 0.05) negative correlations observed between treatment, and both testate amoebae and flagellate abundances. The information obtained from this study may be used to test the relationship between biodiversity and land-use intensity, and how productivity is affected in the pasture. Key words: Soil ecology, pasture management, sustainable agriculture, biodiversity, forage grazing
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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 | 0.000 | 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".