Effects of Local Rural Land Use on Forest Habitats in Southern Ontario
Bibliographic record
Abstract
Forest ecosystems are under severe threat from resource exploitation, fragmentation and disturbance. While the rate of forest loss has slowed in recent years, it is still ongoing, and what remains is increasingly degraded as human development continues. This study investigates some effects of human rural land use on adjacent forest habitats, measured by examining soil function and biodiversity/species composition. Biodiversity was surveyed on four occasions for each taxonomic group over the summer, using quadrat and transect sampling for plants, sweep net and pan trap sampling for insects, and unlimited-distance point counts for birds. Soil function was assessed by determining the rate of decomposition of leaf litter of five major tree species, \nand by investigating soil chemical content. There was evidence of nutrient loading near agricultural sites, which exhibited very high phosphorus, potassium, calcium and nitrogen relative to control. The agricultural sites also had the highest degree of leaf litter mass loss. Agricultural sites contained the highest incidence of invasive species, and the lowest insect and plant diversity. The trailside site contained similar plant diversity to the roadside site, but exhibited the lowest bird diversity. Bird diversity was highest at the control site, but also high at agricultural sites. It would appear the agriculture has the greatest effects on plant and insect life, \nand roads and trails have the greatest impact on bird communities, largely because the primary sources of disturbance from agriculture are chemical in nature, while the disturbance from trails and roads are based on noise and direct disturbance. But these conclusions are based on a limited sample. Overall, land use has significant effects on adjacent ecosystems, presenting difficult questions for ecological restoration in rural and urban environments.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".