Global urban climate and building energy simulations by SLUCM+BEM
Bibliographic record
Abstract
We have developed ILS+Urban: a coupled model of an offline land-surface model (ILS) and an urban canopy and building energy model (SLUCM+BEM) for global urban climate and energy research. The ILS is an offline land-surface model developed by Nitta et al. (2020) that includes MATSIRO (Takata et al. 2003), a land-surface model for the global climate model MIROC5. The SLUCM+BEM is a new parametrisation for urban climate and building energy simulations developed by the authors (Takane et al. 2024), which can simply simulate anthropogenic heat from buildings (QFB) and electricity consumption (EC) from human activities. The model could well reproduce urban air temperature and EC in the Tokyo metropolitan area. We have implemented SLUCM+BEM in ILS, which allows us to simulate global urban climate and building energy with relatively low computational resources in offline mode. A test simulation of ILS+Urban shows that QFB and EC tend to be quantitatively high throughout the year in the Middle East. Shortly, we will implement a global urban database (e.g. global LCZ, anthropogenic heat emissions and morphology, air-conditioning adoption rate) and new technology parameterisations (e.g. EV, PV and heat pump water heaters) for global urban climate and building energy projections and countermeasures for urban heat and energy savings & generation. In addition, the ILS+Urban will be coupled with global climate models (e.g. MIROC and NICAM).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".