Urban agriculture in the transition to low carbon cities through urban greening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban agriculture presents an opportunity to extend food production to cities. This could enhance food security, particularly in developing countries, and allow for adaptation to growing urbanization. This review paper examines current trends in urban agriculture from a global perspective as a mitigation-adaptation approach to climate change adaptation in the midst of a growing world population. Employing vegetation as a carbon capture and storage system encapsulates a soft-engineering strategy that can be easily deployed by planners and environmental managers. In this review, urban agriculture is presented as a land-use solution to counteract the effects of urbanization, and as a means to establish a continuum between cities and the countryside. It espouses the usefulness of urban agriculture to enhance food security while sequestering carbon. As part of urban greening (including newer approaches, such as green roofs and gardens as well as more established forms of greening, such as forests and parks), urban agriculture offers traditionally rural services in cities, thereby contributing to food resources as well as working to alleviate pressing social issues like poverty. It also provides a way to reduce stress on farmland, and creates opportunities for employment and community-building. As part of greening, urban agriculture provides a buffer for pollution and improves environmental (and well as human) health and well-being. This review begins by addressing the physical factors of adopting urban agriculture, such as climate change and development, land use and degradation, technology and management, and experimental findings as well as human factors investigated in the published literature. As such, it presents an integrated approach to urban agriculture that is part of a social-ecological perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it