Microscale air temperature mapping in greater Vancouver, British Columbia
Bibliographic record
Abstract
Background: Mobile air temperature monitoring is a promising method to better understand temperature distributions at fine spatial resolutions across urban areas and to minimize extreme hot weather health impacts. The first study objective was to collect pedestrian microscale air temperature data to evaluate different methods for assessing spatial variation in urban heat exposure in greater Vancouver, Canada. The second objective was to develop microscale land use regression (LUR) air temperature models using the data collected. Methods: Mobile air temperature monitoring was conducted on foot at least twice for 20 routes chosen to represent potential heat exposures. The mobile data were compared with 1-minute measurements from the nearest fixed site, with satellite-derived land surface temperature (LST) for runs corresponding with Landsat overpass days, and with estimates from a previously-developed heat map for the region based on satellite generated geographic data. Six independent variables were considered for use in constructing a 30 x 30m LUR model for each run and within all routes in greater Vancouver. All models were evaluated using a spatial leave-ten-out cross-validation (LTOCV) approach. Results: Mobile measurements were typically higher and more variable than simultaneous fixed site measurements. The relationship between mobile measurements and LST were weak and highly variable. The mobile measurement and heat map z-score differentials suggested that spatial temperature variability was well-captured by the previously-developed heat map. The Distance to Large Water Body, Distance to Major Road, Normalized Difference Water Index, and Sky-View Factor were selected as the most predictive independent variables. On average, the best individual route models explained 38.6% of the variation in microscale air temperatures at 20 routes. The overall model explained only 10.0% of the variation in the route areas of the greater Vancouver region. Conclusion: The microscale measurements confirmed that fixed sites did not characterize the thermal variability within nearby streetscapes. They could also be used to generate LUR models for some locations. The strength of daytime mesoscale atmospheric processes may weaken the predictive power of land use variables. Future studies intending to use microscale modelling should collect data within a restricted time range and across fewer routes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".