Protecting China’s major urban bird diversity hotspots
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework puts forward a new conservation target to enhance urban biodiversity. Cities have a great potential for sustaining biodiversity and nurturing a healthy relationship between people and our nearest nature. It is especially important in developing countries such as China, which has a rich biodiversity and a rapidly growing urban population. Using citizen science data, we show that 48% of the national bird diversity and 42% of its threatened species have been recorded in the top-20 most avian-diverse cities of China. Urban bird diversity hotspots clustered along the eastern coast, indicating the importance of establishing an inter-city conservation network along the East Asian-Australasian Flyway. This urban conservation network would be a starting point to promote social recognition of biodiversity's relational value in a country with a vast population and an increasingly important role in meeting UN's Sustainable Development Goals.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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".