On the Derivation of Material Thermal Properties Representative of Heterogeneous Urban Neighborhoods
Bibliographic record
Abstract
Abstract An important question arises when modeling a heterogeneous landscape (e.g., an urbanized area) with a mesoscale atmospheric model. The surface within a grid cell of the model (which has a typical dimension of one or more kilometers) can be composed of patches of surfaces of different character. The total sensible heat flux in the grid cell, then, is the aggregate of the heat fluxes from each individual surface, each one with a unique thermal response arising from its thermal properties, among other factors. Current methods to estimate the sensible heat flux consider only one (in the case of flat terrain) or three (roof, walls, and ground, for urban areas) active surfaces with thermal properties that are ideally representative of the materials present in the grid cell. The question is then how to choose the representative thermal properties such that the heat flux computed by the model most closely approximates the aggregate of the fluxes from the different patches. In this work a new way to average building material thermal properties for urban canopy parameterizations is presented, and a suite of idealized numerical simulations demonstrates its superiority to two more standard averages. Moreover, this novel approach points to a new way of determining physical properties that are representative of heterogeneous zones.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".