Bibliographic record
Abstract
Hunger is a serious threat to public health; it affects all aspects of children’s lives. As a result of accelerating urbanization rates, agricultural fields have declined, leaving behind food shortages. Over the past few decades, roof farms have emerged as a useful tool in fighting hunger. A limited amount of research has dealt with roof farms from a comprehensive overview, especially those related to schools. Accordingly, this chapter aims to present various experiences of school roof farms that have been selected from different countries. In addition, the challenges these projects faced are explored, as are the pillars of their success, and the benefits accrued by schools and local communities from the roof farm projects. Ultimately, this chapter can guide other schools to take their first steps towards growing their roofs. By reviewing six projects from Vietnam, the United States, England, China, Slovenia, and Canada it was evident that all projects faced problems related to cost, the construction of buildings, and environmental conditions. Nevertheless, all cases succeeded in solving these problems, depending on the four pillars of success. The first pillar is people who collaborated during the whole construction journey until the project came to fruition. The second pillar is the institutional support to provide the required funds. The third pillar is sustainable construction techniques, and finally, following a precise process. Eventually, all the case studies succeeded in creating their roof farm projects and enjoyed their environmental, social, educational, and economic benefits.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".