Study on the Temperature Change Trends and Influencing Factors in Changsha
Bibliographic record
Abstract
As the capital of Hunan Province, Changsha has been rapidly urbanized in recent decades. However, increasing population density, traffic pressure, and energy consumption have a significant anthropogenic impact on the regional climate. Therefore, this paper reviews the spatial and temporal changes in Changsha’s temperature, urban heat island effect, extreme high temperature and heat wave problems, and the application of multi-source data. The study noted a significant rise in Changsha's average annual temperature, an increase in the frequency and intensity of heat waves, and a significant urban heat island effect, primarily driven by urbanization and global warming. Meanwhile, urban blue-green spaces can bring localized cooling of 1 to 3°C, playing a positive role in alleviating high temperatures. To address climate challenges, academics have proposed measures such as low-carbon development, green buildings, protection of blue and green spaces, construction of ventilation corridors, and multi-source data monitoring. However, current research lacks consistency in data, mechanism simulation, and adaptation strategies. Future research recommends strengthening long-term data construction, multi-source data integration, and mechanism research to help Changsha achieve sustainable development.
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".