Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: The exacerbation of asthma and respiratory allergies has been associated with exposure to aeroallergens such as pollen. Within an urban area, tree cover, level of urbanization, atmospheric conditions, and the number of source plants can influence spatiotemporal variations in outdoor pollen concentrations. OBJECTIVE: We analyze weekly pollen measurements made between March and October 2018 over 17 sites in Toronto, Canada. The main goals are: to estimate the concentration of different types of pollen across the season; estimate the association, if any, between pollen concentration and environmental variables, and provide a spatiotemporal surface of concentration of different types of pollen across the weeks in the studied period. METHODS: We propose an extension of the land-use regression model to account for the temporal variation of pollen levels and the high number of measurements equal to zero. Inference is performed under the Bayesian framework, and uncertainty of predicted values is naturally obtained through the posterior predictive distribution. RESULTS: Tree pollen was positively associated with commercial areas and tree cover, and negatively associated with grass cover. Both grass and weed pollen were positively associated with industrial areas and TC brightness and negatively associated with the northing coordinate. The total pollen was associated with a combination of these environmental factors. Predicted surfaces of pollen concentration are shown at some sampled weeks for all pollen types. SIGNIFICANCE: The predicted surfaces obtained here can help future epidemiological studies to find possible associations between pollen levels and some health outcome like respiratory allergies at different locations within the study area.
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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 | 0.000 |
| 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) | 0.001 | 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".