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.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.029 |
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; both teacher heads agree on what is shown here.
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".