Cultivating our urban forest future: a value-chain perspective
Bibliographic record
Abstract
For cities to grow their urban forest canopy the formula appears rather straightforward: the right trees, plus the right conditions, plus the right care equals success. These simplified “tree chain of custody” steps, however, represent activities within a complex value-chain in Canada. Given that there is heightened demand for urban tree planting as natural climate solutions become the norm, how can we prepare the value-chain to meet these demands? To answer this question, we outline the pathways by which trees presently go from nurseries into urban and peri-urban areas. Delineating the actors, roles, and present barriers to success exposes the complexity of the process and relationships in the value-chain, as there are distinct phases with multiple actor groups involved who influence, and are influenced, by one another. We explore the issues that pose prominent challenges to, as well as opportunities for, the value-chain. Emergent themes include communication, forecasting demand and timing, underpricing and undervaluing tree establishment, lack of awareness on the importance of soils, juvenile tree health, species selection, and gaps in evidence-based decision support tools. The touchstones of science and innovation, collaboration, and knowledge mobilization are pertinent for the value-chain in Canada to draw upon to navigate the future.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".