Indicators for Management of Urban Biodiversity and Ecosystem Services: City Biodiversity Index
Bibliographic record
Abstract
Capturing the status and trends of biodiversity and ecosystem services in urban landscapes represents an important part of understanding whether a metropolitan area is developing along a sustainable trajectory or not. However, this task also represents unique challenges for policy makers and scientists alike, challenges that lie at both the methodological (scaling, boundaries, definitions) and institutional levels (integrating biodiversity and ecosystems with social and economic goals). In this chapter we report on the experiences from municipalities in several countries where the newly developed City Biodiversity Index (CBI) has been applied and tested. The purpose here is not to compare or rank different municipalities but rather to deepen our understanding of the science underlying the indicators and contribute improvements to the CBI in different contexts. Based on experiences in implementing the CBI in 14 cities in Japan, and in Lisbon (Portugal), Helsinki (Finland), Mira Bhainder (India) and Edmonton (Canada) it is evident that the CBI has limitations that need to be addressed: (1) lack of data and the scale and boundaries need careful consideration, (2) the scoring represents a challenge as the bio-geographical differences or the profile of the cities varies largely, (3) the number and scope of ecosystems captured are limited and a broader range of ecosystem services should be included, and (4) the integrated social-ecological dimension of cities needs further development. However, it is also evident that CBI has some unique features, and can perhaps most importantly serve as both a tool that brings managers, scientists and other stakeholders together to act on the role of biodiversity and ecosystem services in the cities as well as a tool for assessing the impacts of different policies and land planning options on urban biodiversity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".