Designing Biodiverse Cities for Mental Health and Wellbeing
Bibliographic record
Abstract
Following the Brundtland Report in the early 1990s, the relationship between biodiversity and human wellbeing became a topic of public debate and scientific research [1]. Nowadays, biodiverse cities can provide ecosystem services as well as mental health and wellbeing. Biodiverse cities have a critical role in delivering services and infrastructure, addressing inequity, and regulating environments that influence human health [2]. Several urban health issues may be handled with adequate planning and resources, resulting in mutual advantages for human and environmental health [2]. However, the health effects of biodiversity loss are becoming more well recognised. Ecosystem functioning is affected by biodiversity changes, and substantial ecosystem disturbances can result in life-sustaining ecosystem goods and services [3]. As a result, initiatives for increasing and conserving biodiversity in cities are required. This research examines case studies of urban green infrastructure, best practices, and policies in the United Kingdom and the United States that enhance human health, well-being, and biodiversity conservation. 1) Naeem, S.; Chazdon, R.; Duffy, J.E.; Prager, C.; Worm, B. Biodiversity and human well-being: an essential link for sustainable development. Proc. R. Soc. B Biol. Sci. 2016, 283, 20162091. 2) Secretariat of the Convention on Biological Diversity Cities and Biodiversity Outlook—Executive Summary; Montreal, 2012; ISBN 9292254375. 3) WHO Biodiversity and Health Available online: https://www.who.int/news-room/fact-sheets/detail/biodiversity-and-health (accessed on Aug 5, 2021).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".