Detecting Peri-Urban Climates in China Using a Thermal Variability Framework
Bibliographic record
Abstract
Abstract Nine urban areas in China are examined using a peri-urban thermal variability metric. Of the nine, four showed some evidence of a peri-urban climate that is linked to the location of the climate station within the urban area. A series of metrics that exploit the subtle variation of temperature variability experienced day to day are used to identify the climate station urbanization characteristics from 1951 to 2023. Metrics that identified the local environment as rural, urban, or peri-urban were used. This peri-urban analysis was employed in this work for the first time using Chinese climate data by examining nine urban areas: Harbin, Shenyang, Shijiazhuang, Jinan, Zhengzhou, Hefei, Wuhan, Nanchang, and Changsha. Harbin, Shenyang, Jinan, and Zhengzhou had distinct peri-urban thermal signatures. Harbin, Heilongjiang, a large city in the northeast of China illustrated most clearly the changing thermal variability characteristics as the local climate station experienced the expanding urban reach of the growing city. The location of its climate station at the fringe of the city in 1951 led to a rural classification for many years. This changed dramatically to peri-urban beginning in the 1980s and to urban in the 2010s and into the 2020s, consistent with the expanding urban sprawl. This research provided some initial insight into the interplay of the various metrics used as the site transitioned from rural to peri-urban to urban.
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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 | 0.001 | 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 | 0.001 |
| 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".