Vegetation Effects on Soil Properties in the Monteagle Sandy Loam Series: Implications for Land‐use Change
Bibliographic record
Abstract
Soil properties are strongly influenced by the composition of the surrounding vegetation. We investigated soil properties of three ecosystems; a coniferous forest, a deciduous forest and an agricultural grassland, to determine the impact of land use change on soil properties. Disturbances such as deforestation followed by cultivation can severely alter soil properties, including losses of soil carbon. We collected nine 40 cm cores from three ecosystem types on the Roebuck Farm, north of Perth Village, Ontario, Canada. Dominant species in each ecosystem included hemlock and white pine in the coniferous forest; sugar maple, birch and beech in the deciduous forest; grasses, legumes and herbs in the grassland. Soil pH varied little between the three ecosystems and over depth. Soils under grassland vegetation had the highest bulk density, especially near the surface. The forest sites showed higher cation exchange capacity and soil moisture than the grassland; these differences largely resulted from higher organic matter levels in the surface forest soils. Vertical distribution of organic matter varied greatly amongst the three ecosystems. In the forest, more of the organic matter was located near the surface, while in the grassland organic matter concentrations varied little with depth. The results suggest that changes in land cover and land use alters litter inputs and nutrient cycling rates, modifying soil physical and chemical properties. Our results further suggest that conversion of forest into agricultural land in this area can lead to a decline in soil carbon storage.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".