Characteristics of Urban Forestry Programs in Utah
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it