Bibliographic record
Abstract
Abstract In the face of projected increases in globalization and urbanization, there is growing recognition that cities and their hinterlands will play a pivotal role in both creating and addressing the sustainability challenges of the future. Hinterlands, the rural areas that surround cities, are connected to cities as the source of many of the ecosystem services (ES) that are used in urban areas. While much is known about the provision of multiple ES in and around a few well-studied cities, there is a limited amount of consistently measured, global-scale data about the provision of multiple ES in urban areas and their hinterlands. We mapped eight ES globally, and examined how the production of ES varied between the hinterlands (within 200 km) of 768 major city centers (population > 500 000). We found that there are seven archetypes of ES supply bundles in global hinterlands. Hinterlands near wealthy cities are specialists in regulating ES production while the poorest and most populated hinterlands are specialists in food production, with low levels of regulating and cultural ES provision. These hinterlands also experience different synergies and tradeoffs between ES, with interesting implications for landscape management. Global teleconnections have likely also played a role in the ES bundles of hinterlands, since they have allowed cities to exploit remote areas to meet their demand for ES, undermining the traditional supply-demand relationship between each city and its proximal hinterland. These results emphasize the diverse, and sometimes inequitable, ways that urbanization and globalization are influencing ES supply in the planet’s most human-modified landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".