Evaluation of the ‘local climate zone’ scheme using temperature observations and model simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract ‘Local climate zones’ ( LCZs ) comprise a new and systematic classification of field sites for heat island studies. The classification divides urban and rural landscapes into 17 standard classes, each defined by structural and land cover properties that influence air temperature at screen height. This study is the first to evaluate the conceptual division of LCZs with temperature observations and simulation results from surface–atmosphere models. Results confirm that thermal contrasts exist among all LCZ classes, and that such contrasts are governed largely by building height and spacing, pervious surface fraction, tree density, and soil wetness. Therefore, partitioning of landscapes into structural and land cover classes, or ‘LCZs,’ is deemed justified for the purposes of field site classification in heat island studies. Also justified is the use of inter‐zone temperature difference (Δ T LCZ X−Y ) to quantify heat island magnitude. To further improve the LCZ system, we encourage other researchers to observe and model the climatic conditions of its varied classes. Especially useful would be tests using field data from different urban and rural environments to those in this study, and running more advanced urban canopy models with demonstrated predictive capability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it