Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community‐Partnered Low‐Cost Sensor Deployment
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 The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low‐income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small‐scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra‐neighborhood variability) and compare them to average Vancouver concentrations (inter‐neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low‐cost sensors (LCS) were deployed within the neighborhood to measure PM 2.5 , NO 2 , and O 3 concentrations. The collected 15‐min concentrations were down‐averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM 2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO 2 concentrations and summer O 3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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