Invasive Species Encroach as Shaped by Changes in Land Use Over Time within UTSC’s Forested Property
Bibliographic record
Abstract
Invasive species' inherent capacity to out-compete native plants and change plant community composition; their distribution and abundance across urban woodlots is a growing biodiversity, economic and social concern. While the existing invasive species management efforts continue to focus on controlling invasive species, it is also essential to understand how fragmentation and the legacy of land-use change impact their distribution. Understanding land-use and natural cover changes across the landscape, where they promote invasive species spread, provides conservation and land use planning evidence for developing specific precautionary limits to development projects surrounding woodlots. Our study area includes three urban woodland compartments within the University of Toronto Scarborough Campus. The study focuses on spatial changes and field observations. Spatial changes in the woodland extent and their surrounding area are assessed for the period from 1947 to 2011. The existing distribution and abundance of invasive plants, assessed based on field data obtained from VSP monitoring plots, were interpreted alongside the relevant historical information and land-use change context of the UTSC property. We analyze and discuss the five most common invasive species sampled: common buckthorn (Rhamnus cathartics), dog strangling vine (Cynanchum, rossicum), garlic mustard (Alliaria petiolata), Norway maple (Acer platanoides) and Tartarian honeysuckle (Lonicera tatarica). We show that invasive species monitoring, and management practices would benefit from understanding an area's historical context. Moreover, linking invasive species distribution with land-use change provides insight into the legacy effect of land-use on forest susceptibility to invasive species pressures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".