Bibliographic record
Abstract
More than half of the world's population live in cities, and they have benefitted from the ecosystem services provided by urban biodiversity. International conventions like the Convention on Biological Diversity have provided recommendations on how to engage local governments and cities in the implementation of their strategic plans. In parallel, recognizing this, cities conserve, restore and enhance biodiversity in many practical ways. A list of general strategies adopted by cities is presented. The rich and diverse experiences of eleven cities, i.e. Abu Dhabi, Auckland, Durban, Edinburgh, Edmonton, Hyderabad, Los Angeles, São Paulo, Singapore, Toluca and Vitoria-Gasteiz, which have successfully conserved biodiversity in urbanized landscape across the globe, are shared. These cities have all applied the Singapore Index on Cities' Biodiversity as an evaluation and monitoring tool for biodiversity conservation efforts, in addition to using it innovatively for other purposes. Cities can play a pivotal role in ensuring that Target 12 of the Kunming-Montreal Global Biodiversity Framework will be achieved. Upscaling cities' models of success would require a worldwide concerted effort involving everyone, i.e. all levels of stakeholders. Mainstreaming of biodiversity into all sectors, including commercial, economic, financial, industrial and technological, and the incorporation of biodiversity into decision-making in urban planning and management would be a game-changer. This article is part of the theme issue 'Bringing nature into decision-making'.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.023 |
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".