Modeling Surface Sensible Heat Flux Using Surface Radiative Temperatures in a Simple Urban Area
Bibliographic record
Abstract
Abstract Sensible heat fluxes over a light industrial area in Vancouver, British Columbia, Canada, are analyzed from observed tower fluxes and modeled using a bulk heat transfer approach. The bulk transfer models are initialized using remotely sensed surface temperatures from both airborne and ground-based observing platforms. The remotely sensed surface temperature, in conjunction with a surface database, is used to create area-weighted temperature estimates representative of the complete urban surface. Sensitivity analyses of the various surface temperature estimates are performed. Estimates of kB−1, the ratio of roughness length of momentum to heat, for this area are in general agreement with theoretical estimates for bluff-rough surfaces and are larger than those documented for vegetated and agricultural surfaces. Back-calculated values do vary depending on the method used to determine surface temperature but vary more with the time of day. Empirical relations derived previously for vegetated surfaces are shown to agree well with the results for a dry urban environment. Approaches based on microscale variability in temperature fields are problematic.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".