A One Health Initiative For Air Pollution: Student-Living Gardens
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.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.012 |
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".