Spatial Patterns of Urban Dew and Surface Moisture in Vancouver, Canada, During Summer
Bibliographic record
Abstract
Boundary layer climatology is often concerned with processes on an idealised extensive, homogeneous plain, where a single point sample suffices to characterise surface conditions. Such landscapes are rare but a large, flat field or pasture can be a reasonable approximation. In a patchy landscape, surface characteristics vary spatially and a single point measurement is inadequate. Dew is seldom measured in cities but its accumulation is expected to vary spatially in interesting ways because the city surface is a complicated mosaic of different materials. This study presents the results of a hardware modelling project to study dew (condensation) and surface water (dew + guttation) in an urban residential neighbourhood. A 1/8th scale, out-of-doors model with a simplified geometry was constructed and run in Vancouver, BC, Canada, during summer. The Internal Thermal Mass (ITM) approach to scaling was used to modify the thermal inertia of the model buildings so that nocturnal surface temperatures would be duplicated in real time. It was postulated that dew accumulation (mm d-1) would be also duplicated. Dew, surface temperature and sky view factor in the model varied in explainable patterns, i.e. grass was cooler and wetter at open sites with large sky view, and was warmer and accumulated less dew close to buildings and under trees, where sky view was reduced. This strong association suggests that maps of site geometry expressed as sky view factor could potentially be used to create maps of dew in cities and other patchy landscapes.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".