Short Communication: A Comparative Analysis of Municipal Urban Tree Inventories of Selected Major Cities in North America and Europe
Bibliographic record
Abstract
Effective management of the urban forest calls for municipalities to have a tree inventory of their urban resource. The approach to urban forestry is rather different in Europe and North America, both in terms of background and culture. This contribution discusses similarities and differences in tree inventory practices, based on a pilot study of three major cities in North America (Toronto, Ontario, Canada; and Boston, Massachusetts and New York City, New York, U.S.) and three major cities in Northern Europe (Oslo, Norway; and Aarhus and Copenhagen, Denmark). The pilot study consisted of semi-structured expert interviews in each city, and an analysis of their tree inventories in terms of their level of detail, how they were undertaken, and how they have been used. Each of the cities, with exception of Oslo, had inventoried all of their street trees. Volunteers were only used in Boston and New York City. None of the cities had developed a management plan based on their tree inventory. The inventory had only been completely incorporated into the work order system in New York City and Toronto. This explorative study shows that more research is needed to investigate what subsequently happens to tree inventories in municipalities after they have been performed. Moreover, more work is needed to identify whether inventories are being utilized to their full advantage in terms of producing management plans. Some key themes for further research are described. The set up of this pilot study could serve as a format for comprehensive research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".