A One Health Initiative For Air Pollution: Student-Living Gardens
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
Air pollution is one of the largest issues facing our planet to date. It leads to a variety of severe consequences including an increased incidence of respiratory illness in humans and non-human animals, damage to plants, and exacerbation of climate change. An enormous contributor to air pollution is the livestock farming industry which, in addition to its negative environmental impacts, detrimentally affects the mental health and well-being of non-human animals through various unnatural practices. However, air pollution may be mitigated by planting gardens at homes located in the student-living area of Queen’s University in Kingston, Ontario. These gardens would include vegetables, low-maintenance plants, and edible native species which would remove toxins from the air and provide multiple additional benefits to humans, non-human animals, and the environment. One of the greatest benefits of the proposed gardens would be the provision of vegetables and edible native species, allowing students to consume more plant-based foods and stray away from livestock consumption. The gardens would also increase ecosystem biodiversity, which would not only make plant life more resilient but also help create new opportunities for reliable food sources and habitats. If the success of the proposed initiative were to be proven within the area, additional strategies may be introduced in Kingston to further reduce air pollution and perhaps inspire other university communities to undergo similar changes.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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