The “Clean\nAir Outreach Project”: A\nPaired Research and Outreach Program Looking at Air Quality Microenvironments\naround Elementary Schools
Bibliographic record
Abstract
The City of Kitchener is the largest city in Waterloo\nRegion in\nthe province of Ontario, the third fasting growing region in Canada,\nyet it has only one air quality monitoring station. Our research group\nlaunched a pilot project in September 2020 to install a network of\nAQMesh multisensor mini air quality monitoring stations (pods) near\nelementary schools in Kitchener. Here, we describe an outreach and\neducational project (The Clean Air Outreach Project), which we launched\nin May 2021 for elementary-school-aged students attending schools\nnear the pods. The primary goal of this project was spreading awareness\nabout air quality and its connection to health impacts, principles\nof chemical reactions in the atmosphere, and climate change. The project\ncontinued until December 2021. Virtual presentations were delivered\nby a team of undergraduate university students to a total of 350 students\nin grades 5–8. Student knowledge was assessed using poll questions\nand Kahoot games. Follow-up interviews were conducted with the teachers,\nwho reflected on the impact and educational elements of our presentations.\nThe outcomes of this outreach project and teachers’ feedback\nrevealed that such initiatives can spark interest in scientific knowledge\nin general and engagement in environmental issues at the school and\ncommunity levels.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 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 teacher head, 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".