Comparing urban food systems between temperate regions and tropical regions introducing urban agroforestry in temperate climates through the case of Budapest
Bibliographic record
Abstract
The aim of this paper is firstly to assess what makes urban agriculture more successfully integrated in some cities than others.Secondly, to introduce agro-ecology practices in public green open spaces and community gardens through a landscape assessment and a map-based comparison analysis.The main problem is to motivate the planners to integrate urban agriculture in the Urban plan.Therefore, these green structures with their benefits cannot be part of the city's landscape and dynamic.The sustainability of food systems depends on the planning strategy of the city and the governance policies.Whilst urban agroforestry is well applied in Tropical Climate, it has not been fully explored in Temperate climate.This practice could have multiple functions in the Temperate Region and become a sustainable land use thanks to agro-ecology principles.After defining urban agroforestry for Temperate Regions, a methodology to find the best spaces to introduce agro-ecology practices will be evaluated through the case of Budapest where a green infrastructure plan has recently been launched and an agroforestry project is being initiated.This paper concludes that urban agroforestry is a sustainable land use that can better integrate food systems in the city.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".