Connecting Canopies: Portland-Vancouver Regional Urban Tree Policy and Program Summary
Bibliographic record
Abstract
Trees are crucial green infrastructure in a changing climate, but urban trees face an array of threats as cities grow and redevelop. Without plans, policies and programs for their conservation, tree loss undermines the long-term health and viability of urban communities. We developed a framework to summarize and compare urban tree policies and programs for the Portland-Vancouver metropolitan region, using interviews and a cross-comparison of codes, investment and staff levels, and other features. Across the region, urban tree policies and programs differ among 42 distinct jurisdictions. Most communities have tree codes, but they vary in their strength and comprehensiveness. Staffing and management levels for trees also vary and are frequently split across departments within a jurisdiction. Few jurisdictions have tree or canopy cover inventories, and fewer use these inventories to direct tree management and investments. Investment levels in trees are difficult to ascertain and variable from one jurisdiction to another. Community partnerships and workforce development programs for trees are also inconsistent and frequently absent. Most community partnerships rely on volunteers, and few invest in jobs to plant and care for trees in low tree canopy settings. Despite their ecological significance, urban tree policy and program information for the Portland-Vancouver region is difficult to access, and governance is fragmented with no minimum standards of protection or care. We compared our findings with two prior regional urban tree policy and program assessments from 2000 and 2010, to suggest that limited progress has been made over the past 20 years.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".