Employing an urban meteorological network to monitor air temperature conditions in the ‘local climate zones’ of Szeged, Hungary
Bibliographic record
Abstract
ABSTRACT We analyse the average annual and seasonal air temperature conditions in the ‘local climate zones’ ( LCZs ) of Szeged, Hungary. The basis of our analysis is a 1‐year dataset from 2014 to 2015 for a 20‐station urban meteorological network. The network and its corresponding LCZ classes put temperature studies in Szeged into a new spatial framework to assess local climate and urban heat island (UHI) conditions. The stations were installed at locally representative sites using a Geographic Information System (GIS) method based on the standard surface parameters of the LCZ classification. The network was purposely designed to monitor thermal differences among LCZ classes in Szeged. We provide detailed site metadata for each of the monitoring stations used in the analysis. Our results show that the densely built‐up LCZ classes have higher annual and monthly mean and minimum air temperatures than structurally open and more vegetated classes, with nocturnal differences of >4 °C observed under calm, clear skies. Among select temperature indices measured in the urban LCZ classes, frost days, cooling degree‐days, and tropical nights differ markedly from the background rural LCZ classes. This difference suggests that local climatologies exist within Szeged, and that these have implications for thermal comfort, urban energy use, and urban agriculture. Finally, the evaluation of heating and cooling rates in Szeged shows an important role for LCZs in UHI analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".