Characteristics of Urban Forestry Programs in Utah
Bibliographic record
Abstract
Urban/community forestry programs in Utah, U.S., were studied; a questionnaire was sent to community forestry contacts in every incorporated community in the state in summer 2002. Respondents reported on program support, budget, management authority and practices, strengths and weaknesses, and training and information needs. Program support from residents, town officials, and employees was fairly strong, with 80% indicating some support. One-quarter of towns have a tree board and celebrate Arbor Day. Towns obtain assistance from nurseries or tree care businesses, Extension, and state forestry, in that order. Two-thirds of communities have a tree-related budget, with a mean budget of US$44,000 and a median budget of $3,000, averaging $2.58 per resident and $25.16 per tree. Total budget generally increased with population, but the smallest towns had the largest per capita and per tree budgets. Most towns spend enough to qualify for Tree City USA’s requirement of $2 per capita. The ratio of spending for maintenance versus planting increased from 0.6 for small towns to 4.1 for larger cities. Just under two-thirds of communities have forestry programs. The average number of public trees per town is about 2,300 (median 150), with numbers of trees increasing as population increased, but with trees per capita generally decreasing as population increased, ranging from 0.21 to 0.43 trees per person
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".