Positive or Negative? Urbanization‐Induced Variations in Diurnal Skin‐Surface Temperature Range Detected Using Satellite Data
Bibliographic record
Abstract
Abstract Diurnal temperature range (DTR) is an important indicator for assessing the local climate change due to urbanization. Studies that focused on surface air temperature (SAT) have reported decreased DTRSAT in urban areas. However, this urbanization‐induced effect becomes more complex with regard to land skin‐surface temperature (LST), which is highly localized and extremely sensitive to land surface properties. We thus investigated the urban‐rural DTRLST difference (ΔDTRLST) over 354 cities across China using satellite‐retrieved LSTs within 2008−2013. Our major findings include the following: (1) urban areas experience increased (decreased) DTRLST compared with rural areas on the annual average for the majority of cities located in southern (northern) China; (2) the ΔDTRLST is mostly positive in warm months but negative in cold months. It generally becomes more positive from January to August and becomes more negative afterward; and (3) the ΔDTRLST is positively related to the daytime surface urban heat island intensity; it is yet negatively correlated with the urban‐rural difference in vegetation abundance. We consider these insights valuable for in‐depth understanding urban thermal environment and will likely help improve urban planning.
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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 | 0.000 | 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) | 0.001 | 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".