Bibliographic record
Abstract
We examined two of humanity's present-day challenges, climate change and chronic diseases, in relation to the co-benefits that green spaces provide to human health and the environment. The reduction of several chronic diseases and associated symptoms, including anxiety, obesity and cardiovascular disease, has been associated with the presence of and access to green space. Green spaces also contribute to a number of environmental health benefits and have been shown to reduce the likelihood of flooding, improve air quality and provide cooling and shade. These co-benefits address both the symptoms of several chronic diseases and associated risk factors along with the environmental and health impacts of climate change. This article explores how to maximize the co-benefits of green spaces through two examples of multi-sectoral collaborations. With these two examples, we have provided a model of collective collaboration that aims to address complex issues, such as climate change and chronic diseases, through the common intervention of green spaces.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.054 | 0.035 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".