The Relationship between Ecosystem Services, Human Health and Well-being and its Implication for Environmental Planning: An Agent-Based Model and Geosimulation
Bibliographic record
Abstract
A tremendous number of studies have examined the relationship between ecosystem health and human health and well-being, especially in urban settings. The project builds upon a nation-wide Canadian study by Crouse et al. (2017) which explored the correlations between urban greenness and cause-specific mortalities using Cox proportional hazard ratios. Crouse et al. (2017) concluded that increased urban greenness in proximity to participants’ residences is associated with decreases in the risks of cause-specific mortalities. The goal of this project is to develop an agent-based model using NetLogo to explore the relationship between ecosystem services, human health and well-being in the Credit River Watershed (CRW). The model utilizes a Normalized Difference Vegetation Index (NDVI) to establish values of urban greenness in the CRW. Then, hazard ratios are calculated from these NDVI values based on the association observed in the Crouse et al. (2017) study. The model uses a tree-planting, or greening, agent that changes the values of greenness in the study area, thus decreasing hazard ratios. The greening agent is counteracted by a developer agent which converts land adjacent to residential \nareas into new development. Consequently, this action decreases greenness and increases hazard ratios. This interaction occurs overtime and the results are shown through geosimulation. The model also provides a set of user-defined parameters that modify the nature of agent interactions and the underlying rules governing the model. Overall, the model serves as an educational and decision-support tool for stakeholders in the CRW, including residents, municipal planners, conservation authorities, and policy makers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".