Investigating Mechanical Damage to Trees at the Toronto District School Board (TDSB)
Bibliographic record
Abstract
The Toronto District School Board (TDSB) is the second-largest landowner in the City of Toronto, managing over 36,000 trees across 2,057 hectares and 611 properties. TDSB's "Large Tree Program'' has facilitated the annual planting of 300 trees on schoolyards since 2007. However, one of the limits to the long-term growth and sustainability of the TDSB's urban forest is the prevalence of avoidable damage to trees by landscaping equipment, particularly lawnmowers. This paper aims to raise awareness of the current state of lawnmower damage at the TDSB and provide recommendations for tree protection from mechanical damage. A tree inventory of 19 schools conducted during the summer of 2021 enabled the preliminary assessment of the current state of mechanical damage at the TDSB. This survey is the first to quantify the extent of mechanical damage by lawn maintenance equipment in North America. Recommendations for tree protection were developed from a review of literature related to the mechanical damage of trees and alternative landscape management practices.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".