Planetary health risks in urban agriculture
Bibliographic record
Abstract
Urban agriculture is gaining recognition for its potential contributions to environmental resilience and climate change adaptation, providing advantages such as urban greening, reduced heat island effects, and decreased air pollution. Moreover, it indirectly supports communities during weather events and natural disasters, ensuring food security and fostering community cohesion. However, concerns about planetary health risks persist in highly urbanized and climate-affected areas. Employing electronic databases such as Web of Science and PubMed and adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we identified 55 relevant papers to comprehend the planetary health risks associated with urban agriculture. The literature review identified five distinct health risks related to urban agriculture: (1) trace metal risks in urban farms; (2) health risks associated with wastewater irrigation; (3) zoonotic risks; (4) other health risks; and (5) social and economic risks. The study highlights that urban agriculture, while emphasizing environmental benefits, particularly raises concerns about trace metal bioaccumulation in soil and vegetables, posing health risks for populations. Additionally, risks associated with wastewater irrigation and backyard livestock farming and gaps in infectious disease research were identified. While the systematic literature review underscores the increasing attention to urban agriculture, success in this field necessitates addressing safety concerns through meticulous planning and stakeholder engagement.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".