Toronto’s heritage trees: A Living Seed Bank for Forest Restoration
Bibliographic record
Abstract
The City of Toronto committed to raising the canopy cover from 27% to 40% within 50 years and thus a large number of seeds are required because they are the basic elements in the forest restoration process. Native heritage trees can serve as a living seed bank to provide local seeds for healthy forest restoration due to their adaptation to local climate and biodiversity. However, no specific studies and guidelines were found for seed collection from native heritage trees in Toronto. To develop a Toronto-specific database, we mapped native heritage trees in 14 ravines within the Don River in GTA. This is the fourth year of this project. Improvements to the existing database include the discovery of black maple and the addition of 162 trees of 15 native species. Combined four-years of effort, only 33% out of the 73 total native species in Toronto were found in Toronto’s ravines, with the highest ravines (Park Drive and Sunnybrook Park) containing 22% (16 native species). Under limited resources, we developed a Species Priority Rank to help prioritize our efforts, which was based on 73 Toronto’s native species’ seed production interval, population abundance and diseases that affect trees’ growing and health. And finally, we suggested focusing on 35 high priority native species. This work will make it easier for people to collect seeds from heritage native trees, restore the forest in a number of ways and increase the ability and efficiency of Toronto to restore its forest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".