Evaluation of Changes in Urban Forests in the Annex Neighbourhood, Toronto from 2011 to 2022
Bibliographic record
Abstract
Urban forests play an important ecological role in urban society's well-being. Given their functionalities, they are regarded as an indispensable key to developing green and sustainable communities. Nevertheless, rapid urbanization in recent years has threated urban trees' survivorship and increased their vulnerability to numerous environmental stresses. The Annex Neighbourhood, as one of the communities in Toronto and the only one that its urban forest has been undergone with monitoring, is an appropriate candidate where evaluation of changes in urban forest over 11 years-period can be developed. Tree inventory data collected in 2011 and resampled in 2022 are compared and analyzed based on species richness, canopy cover and basal area either at community or parcel level. Results show that while positive changes are observed in tree growth (reflected by DBH and basal area) and species richness, the community also experiences a net loss of trees and canopy cover, along with a slight decline in its species evenness and Shannon Wiener Index at the parcel level. The importance value also shows that Acer is the most dominant species in both periods. Possible reasons behind decreasing trend in forest cover and diversity could be the lack of diverse tree species being planted from 2011 to 2022, increased tree mortality due to intensified environmental stresses, and insufficient rate of tree replantation. To preserve urban forests and enhance their ecosystem services in the Annex Neighborhood, a sustainable forest stewardship programme is recommended by strengthening local residents' education, implementing a regular tree monitoring programme, assisting in protecting surviving trees and planting diverse tree species strategically with homeowners in the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".