Can Surface-Cover Tiles Be Summed to Give Neighborhood Fluxes in Cities?
Bibliographic record
Abstract
Abstract The paper addresses the question of whether the modeling practice of summing separate land-cover tiles to give urban fluxes at the neighborhood scale has merit. A central-city site in Basel, Switzerland, was instrumented to measure turbulent sensible heat fluxes QH from the two main land-cover types (roofs and canyons) separately and from the whole neighborhood. Path-averaged QH values were measured in the roughness sublayer (RSL) using scintillometry, and the spatially averaged QH neighborhood-scale flux was measured in the inertial sublayer (ISL) by an eddy-covariance system. The roof and canyon flux results are combined and weighted according to the respective plan-area abundance of each to give an estimated value of the neighborhood flux. The results show that this “bottom up” approach underestimates the measured ISL values by about 25% when averaged across all periods and wind directions. This finding led to consideration of possible errors from instrumentation, inappropriate turbulent source areas, failure to sample representative surfaces, and inability to fully capture RSL heat exchange. Sorting data by the two main wind directions revealed significant differences. The measured fluxes in the ISL and across the canyon top depend little upon wind direction, but daytime roof values show a marked sensitivity to wind direction. Qualitative analysis suggests this might be caused by systematic controls such as solar angle, site morphometry, and observational setup. The comparison of bottom up versus ISL is inconclusive; in some conditions agreement appears promising, and in others it does not. The question has not been proven or disproven. It may be too ambitious to test the concept at a real-world site.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".