Leveraging Crowdsourced Data for Extreme Heat Monitoring
Bibliographic record
Abstract
ABSTRACT The combined effects of urban microclimate heterogeneity and climate change exacerbate the disproportionate impact of heatwaves on urban areas, a trend expected to intensify. Crowdsourcing is a promising tool to monitor temperatures at a high spatiotemporal scale, which is now deemed critical. However, quality control is essential before the use of such data. Traditional quality control methods often fail to capture short extreme weather events like heatwaves, as they frequently eliminate crucial observations from these intense, brief episodes. Here, a quality control methodology, tailored to short‐term heatwaves built on existing quality control methods, is introduced and tested on crowdsourced monitoring networks for five North American cities and three heatwave episodes. This framework is centred around a systematic comparison with traditional weather stations. The results show that the designed procedure can effectively filter out false data points and corrupt stations whilst preserving observational data points capturing heatwaves. In the worst case, 24.7% of a heatwave episode's records are eliminated, compared to 75% using an existing detailed quality control method. We further show that crowdsourced monitoring could bring more insight into the spatiotemporal variability of temperature and living experiences during heatwaves compared to the sparse traditional weather station networks.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".