Incorporating Community Knowledge Into Analysis of Air Quality Monitoring Network Data
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
Abstract We conducted a pilot study to explore methods of incorporating qualitative community knowledge into quantitative assessment of temporal and spatial air quality patterns in a neighborhood in Vancouver, British Columbia. We deployed a low‐cost sensor network measuring NO, NO 2 , and PM 2.5 . We used a variety of sources of community knowledge to complement a timeseries analysis and spatial model: a survey by the residents' association; odor reports from a citizen science project; and data from a community mapping event. Community knowledge highlighted, among other sources, areas where cars and heavy‐duty vehicles idle, locations of construction, and locations of wood stoves. When creating a “traditional” land use regression (LUR) using easily accessible, and publicly available data sources, and a “community” LUR that uses community‐reported air pollution sources, model fit was improved in the community LURs for NO 2 and NO x . This suggests that community knowledge can provide insight into sources that are not well captured in commonly used, publicly available data sets due to their transient and informal nature. Not all community‐reported short‐term events corresponded to peaks in monitor data, which could reflect that reports were more correlated with unmeasured pollutants. We suggest that future studies collecting community knowledge on short‐term pollution events through community mapping lower barriers to participation (i.e., through hosting a series of drop‐in events, providing childcare, or timing any event to coincide with neighborhood‐wide events). With these examples, we showcased ways to include community knowledge in quantitative air pollution studies and highlight opportunities to expand on these methods.
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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 | 0.007 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it