Geographically evaluating urban-wildland juxtapositions across 36 urban areas in the United States
Bibliographic record
Abstract
As human populations become concentrated in larger, more intensely urbanized areas connected through globalization, the relationships of cities to their surrounding landscapes are open to social, ecological, and economic reinterpretation. In particular, the value of access to nature in the form of nearby undeveloped wildland to urban populations implies a relatively novel type of synergistic city-region relationship. We develop a robust and replicable metric – the urban-wildland juxtaposition (UWJ) – that quantifies critical dimensions of the juxtaposition of the urbanicity of cities with the quantity of nearby unbuilt wildlands, based on the spatial proximity and relative intensities of these two contrasting system types. Using a distance-decay gravity model, this analysis provides documentation on the calculation of the UWJ and its component metrics, urbanicity (U) and wildland (W) and then presents U, W, and UWJ metrics for 36 urbanized areas representing all regions of the U.S., providing the basis for comparisons and analysis. We explore the potential of the metric by testing correlations with “creative class” employment and public health measures. The UWJ has implications and potential applications for demographic, economic, social, and quality-of-life trends across the U.S. and internationally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".