Is Evidence-Based Conservation Applied in Urban Forestry? A Case Study from Toronto, Canada
Bibliographic record
Abstract
Evidence-based conservation seeks to incorporate sound scientific information into environmental decision making. The application of this concept in urban forest management has tremendous potential, but to date has been little applied, largely because existing scientific studies emphasize the importance of urban forests in large-scale ecological and anthropogenic processes, but in practice, scientific evidence is ostensibly incorporated into North American urban forest management only when deciding the fate of individual trees. Even under these disjunctive conditions, the degree to which evidence influences tree-level decisions remains debatable. In analyzing preliminary data from a case study from Toronto, Canada, we sought to test if and how scientific evidence factored into the decision to remove or preserve 53 trees, located in close proximity to a provincially significant area of natural and scientific interest (ANSI). We found that by far the strongest tree-level correlate of the recommendation to remove or preserve trees was whether or not an individual tree was in conflict with proposed development. In comparison, species identity, tree condition, and suitability for conservation were statistically unrelated to the final recommendation. Our findings provide the basis to expand our analysis to multiple case studies across Canada, and internationally. Furthermore, when interpreted with available research and policy, our preliminary (and future) analysis highlights clear opportunities where scientific evidence can and should be readily incorporated into urban forestry management and policy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".