Analysis of change in the distances between global terrestrial protected areas and urban areas
Bibliographic record
Abstract
With the expansion of urban areas and protected areas (PAs), the distance between them is strongly declining.However, this phenomenon hasn't garnered much attention.The negative influences of urban areas on PAs have scaling effects, and with this distance decreasing, those negative influences may compound, therefore the distance between PAs and urban areas could be an important reference for measuring these negative influences.Based on spatial data of PAs, cities and urban areas, our study analyzed the changes in distance from PAs to urban areas between 1950 to 2010 at global, continental, regional and national scale.The results showed that: (1) at these four scales, the distance between PAs to urban areas were all declining.Europe (Western Europe) was the continent (region), which had the closest proximity of PAs and urban areas.On the contrary, Oceania (Australia and New Zealand) was the continent (region), which had the farthest proximity of these areas.Among the top 20 PAs countries, China had the nearest proximity, as the mean distance from PAs to cities with more than 50 thousand people was merely 143.5 km.(2) According to the current situation and changes in the distances between PAs and urban areas, the top 60 PAs countries can be divided into 5 categories: (a) the proximity was very near and the speed of changes was slow, such as Western European countries; (b) the proximity was near and the speed was moderate, such as China and America; (c) the proximity was relatively near and the speed was rapid, such as Saudi Arabia and Ecuador; (d) the proximity was relatively distant and the speed was relatively slow, such as Brazil, Canada and Russia; (e) the proximity was distant and the speed was relatively rapid, such as Australia and most African countries.(3)On a global scale, more and more PAs with high biodiversity are influenced by urbanization.This study may draw attention and awareness to the changing proximity between PAs and urban areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".