What can a midsized, semi-arid city teach us about human-made forests?
Bibliographic record
Abstract
Abstract Research has shown that urban tree canopy (UTC) provides a multitude of ecosystem services to people in cities, yet the benefits and costs of trees are not always equitably distributed among residents and households. To support urban forest managers and sustainability planning, many studies have analyzed the relationships between UTC and various morphological and social variables. Most of these studies, however, focus on large cities like Baltimore, MD, Los Angeles, CA, and New York, NY. Yet, small and midsized cities are experiencing the most growth globally, often having more opportunity to alter management strategies and policies to conserve and/or increase canopy cover and other green infrastructure. Using both a linear and spatial regression approach, we analyzed the main drivers of UTC across census block groups in Fort Collins, CO, a midsize, semi-arid city projected to undergo significant population growth in the next 20-30 years. Results from Fort Collins indicated that block groups with older buildings and greater housing density contained more UTC, with 2.2% more canopy cover for every 10 years of building age and 4.1% more for every 10 houses per hectare. We also found that distributional inequities may already be developing within this midsized city, as block groups with more minority communities were associated with lower UTC. We compared the drivers of UTC in Fort Collins to other cities located in different climate regions, or biomes, and in various stages of urban development. Based on these results, we suggested future urban forest management strategies for semi-arid cities like Fort Collins.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".