Land Subsidence in Beijing’s Sub-Administrative Center and Its Relationship with Urban Expansion Inferred from Sentinel-1/2 Observations
Bibliographic record
Abstract
Beijing’s Sub-Administrative Center (BSAC) is located in the South-eastern Beijing Plain, which exhibits severe subsidence. The rapid urban expansion in recent years has aggravated land subsidence and threatens the safe operation of Beijing. First, this study applied the persistent scatterer-interferometric synthetic aperture radar (PS-InSAR) to extract BSAC subsidence time series data. Second, combined with the index-based built-up index (IBI), expansion intensity index (EII), and expansion gradient index (EGI), the spatiotemporal characteristics of urban expansion were retrieved from optical data. Finally, we examined the urban expansion effects on land subsidence at the regional and single-building scales. The results showed that the maximum subsidence velocity in the BSAC reached 121 mm/year from 2015 to 2018, and the urban construction land area increased by 22%. At the regional scale, there existed a positive correlation between land subsidence and EGI or EII. This indicated that urban expansion had a certain impact on land subsidence. Therefore, we further explored the relationship between construction and land subsidence at the single-building scale. The engineering construction effects on land subsidence were divided into three periods, namely, rapid settlement, rebound, and stable periods. Although construction had a significant influence on land subsidence, it did not cause subsidence mutation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".