Supplementary Data for "Incorporating Community Knowledge into Analysis of Air Quality Monitoring Network Data"
Bibliographic record
Abstract
These are the underlying data sets needed to perform the peak analysis and create the land-use regression (LUR) models described in the paper. There are four datasets: "RAMP_Location.csv": Locations and IDs of the low-cost sensors used in this work. [NOTE: latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.] "RAMP_data.zip": This contains the csv files of calibrated PM2.5, NO, NO2, CO and O3 measurements for the entire study period across all low-cost sensor sites. "Vancouver_Population_Density_2016.zip": Shapefile of the population within each Dissemination Area from the 2016 Canadian Census. This information was originally extracted from the Canadian Census Analyser supported by the University of Toronto. "smell_van_data.csv": Contains the locations, date, and description of odor reports during the monitoring period from the Smell Vancouver website (https://smell-vancouver.ca) There is also sample code in Python to perform the peak analysis and create the LURs. [NOTE: we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.] "Peak_analysis_geohealth.py": A Python script to perform the peak analysis from the paper. "LUR_strathcona_geohealth.py": A Python script to create the LURs and maps of LUR results from the paper. Links to other data used in the code from publicly available sources: Railway locations - https://opendata.vancouver.ca/explore/dataset/railways/information/ Public streets - https://opendata.vancouver.ca/explore/dataset/public-streets/information/ Land use - https://open-data-portal-metrovancouver.hub.arcgis.com/datasets/metrovancouver::landuse-2016-code-description/about Bus stops - https://abacus.library.ubc.ca/dataset.xhtml?persistentId=hdl:11272.1/AB2/QQLSCJ Block outlines - https://opendata.vancouver.ca/explore/dataset/block-outlines/information/
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.533 | 0.180 |
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".