Status and development needs of urban forest inventories in British Columbia, Canada
Bibliographic record
Abstract
Many Canadian cities have begun developing urban forestry strategies that provide strategic guidance for the preservation and revitalization of their urban tree populations and urban green spaces. However, while tree inventory programs are often identified as the cornerstone of such strategies, a standardized practical framework for tree inventory practices, specifically designed for urban forests, has been slow to emerge. This research: 1) examines how urban forest inventories are understood by British Columbia (BC) municipalities; 2) evaluates the status of, and identifies trends in, BC municipal urban forest inventory programs for the first time, and 3) identifies inventory elements and provides recommendations for a more standardized and enhanced municipal urban forest inventory program in BC. A total of 162 BC municipalities were invited to share information on their municipal urban forest inventory programs via an online questionnaire. The questionnaire response rate was 50%. Key findings included the discovery that urban forests and urban forest inventory programs were not well understood and managed in a consistent manner across BC municipalities, and that this trend is likely to continue in the absence of a concerted effort to reverse it. Increased municipal expenditures on parks, culture and recreation was positively correlated to the existence of an urban forest inventory system, among other contributing factors such as the presence of an urban forest strategy, the location of the municipality and municipal policies. Street trees were identified as the most common focus of BC municipal inventory activities, and inventory data were commonly used for both strategic planning and daily operations. Based on these findings, I proposed a list of recommendations for the development of an approach to urban forest inventory programs for BC municipalities that is sensitive to local circumstances.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".